842 citations as recorded by crossref.

1. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al. 10.1007/s11269-023-03552-7
2. Minor Soil Elements in Contrasting Profiles in an Area Frequently Affected by Fire, NE Iberian Peninsula M. Francos et al. 10.3390/fire5060189
3. Enhancing Accuracy of Forecasting Monthly Reservoir Inflow by Using Comparison of Three New Hybrid Models: A Case Study of The Droodzan Dam in Iran S. Khorram & N. Jehbez 10.1007/s40996-024-01418-5
4. Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning J. Hagen et al. 10.1016/j.jhydrol.2021.126086
5. Exploring hydrological system performance for alpine low flows in local and continental prediction systems A. Chang et al. 10.1016/j.ejrh.2024.102056
6. Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. 10.1016/j.jhydrol.2020.125605
7. Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
8. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
9. A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds G. Saha et al. 10.1016/j.scitotenv.2023.162930
10. An active learning convolutional neural network for predicting river flow in a human impacted system S. Reed 10.3389/frwa.2023.1271780
11. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann 10.1016/j.cageo.2021.104708
12. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
13. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. 10.1016/j.jhydrol.2023.129199
14. A gradient-enhanced sequential nonparametric data assimilation framework for soil moisture flow Y. Wang et al. 10.1016/j.jhydrol.2021.126857
15. A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea K. Kareem et al. 10.3390/w14142172
16. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi 10.3390/resources11120111
17. Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction? V. Moosavi et al. 10.1016/j.asoc.2024.111425
18. On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
19. IoT-Gewässergüte-Monitoring mittels KI-basierter Hauptionenzerlegung aus Leitfähigkeitsdaten im Erfteinzugsgebiet M. Delker et al. 10.1007/s35147-023-1874-7
20. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
21. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
22. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
23. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
24. Comprehensive comparison of artificial neural networks and long short-term memory networks for rainfall-runoff simulation G. Mao et al. 10.1016/j.pce.2021.103026
25. Unraveling the Effects of Long-Distance Water Transfer for Ecological Recharge W. Ding et al. 10.1061/(ASCE)WR.1943-5452.0001272
26. A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating H. Hu et al. 10.1007/s11269-021-02990-5
27. Imputation of missing sub-hourly precipitation data in a large sensor network: A machine learning approach B. Chivers et al. 10.1016/j.jhydrol.2020.125126
28. Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model X. Yang et al. 10.3390/w14132018
29. Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest H. Yin et al. 10.1016/j.jhydrol.2022.128813
30. Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction A. Meyal et al. 10.3389/frwa.2020.574917
31. Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
32. Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models J. Khatti & K. Grover 10.1016/j.jrmge.2022.12.034
33. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
34. Quantifying future water resource vulnerability in a high-mountain third pole river basin under climate change C. Chai et al. 10.1016/j.jenvman.2024.121954
35. Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada P. Parisouj et al. 10.1080/19942060.2023.2242445
36. Developing data-driven learning models to predict urban stormwater runoff volume R. Wood-Ponce et al. 10.1080/1573062X.2024.2312514
37. Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations V. Pellet et al. 10.1088/2515-7620/ad7adb
38. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. 10.1016/j.jhydrol.2024.130861
39. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
40. Simulation-based inference for parameter estimation of complex watershed simulators R. Hull et al. 10.5194/hess-28-4685-2024
41. Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks B. Gao et al. 10.1016/j.agrformet.2024.109907
42. A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon F. Dtissibe et al. 10.1016/j.sciaf.2023.e02053
43. Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting S. Oruc et al. 10.3390/w16233465
44. Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network C. Chen et al. 10.3390/s21041238
45. Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
46. A framework to assess multi-hazard physical climate risk for power generation projects from publicly-accessible sources T. Luo et al. 10.1038/s43247-023-00782-w
47. Structural-optimized sequential deep learning methods for surface soil moisture forecasting, case study Quebec, Canada M. Zeynoddin & H. Bonakdari 10.1007/s00521-022-07529-2
48. Application of LSTM considering time steps in runoff prediction of Ganjiang River Basin H. Leyi et al. 10.18307/2024.0454
49. Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California S. Adams et al. 10.5194/hess-27-3021-2023
50. A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. 10.3390/w13243633
51. Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate J. Huang et al. 10.1007/s11227-022-04827-3
52. Characteristics and Prediction of Reservoir Water Quality under the Rainfall-Runoff Impact by Long Short-Term Memory Based Encoder-Decoder Model X. Sheng et al. 10.1007/s11269-024-04033-1
53. A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series H. Zuo et al. 10.1007/s11269-023-03681-z
54. Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models S. Khorram & N. Jehbez 10.1080/19942060.2024.2387051
55. Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2 T. Dantzer & B. Kerkez 10.1016/j.envsoft.2024.106137
56. Comparison of data-driven techniques for daily streamflow forecasting P. de Bourgoing & A. Malekian 10.1007/s13762-023-05131-0
57. An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points Y. Liu et al. 10.3390/app122312334
58. Analysis of reservoir outflow using deep learning model S. Choudhary & S. Ghosh 10.1007/s40808-023-01803-5
59. Evolution and attribution of ecological flow in the Xiangjiang River basin since 1961 W. Guo et al. 10.1007/s11356-023-29626-y
60. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. 10.1016/j.envsoft.2020.104926
61. Dynamic prediction of mechanized shield tunneling performance R. Wang et al. 10.1016/j.autcon.2021.103958
62. Prediction of energy consumption in campus buildings using long short-term memory M. Faiq et al. 10.1016/j.aej.2022.12.015
63. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al. 10.1016/j.ejrh.2024.101720
64. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. 10.1038/s43017-023-00452-7
65. A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River H. Xue et al. 10.3390/su15107819
66. Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. 10.5194/essd-13-2001-2021
67. Towards a better consideration of rainfall and hydrological spatial features by a deep neural network model to improve flash floods forecasting: case study on the Gardon basin, France B. Saint-Fleur et al. 10.1007/s40808-022-01650-w
68. Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory R. Yuan et al. 10.3389/feart.2021.621780
69. Modern Strategies for Time Series Regression S. Clark et al. 10.1111/insr.12432
70. Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms T. Kesavavarthini et al. 10.1002/joc.8056
71. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. 10.1016/j.jhydrol.2024.131867
72. Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition J. Shen et al. 10.3390/w14142241
73. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
74. Multi-station runoff-sediment modeling using seasonal LSTM models V. Nourani & N. Behfar 10.1016/j.jhydrol.2021.126672
75. A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation M. Mirzaei et al. 10.3390/su132313384
76. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin S. Nimai et al. 10.3390/w15213758
77. Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model C. Mauricio et al. 10.3389/fspas.2024.1442315
78. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
79. Improving streamflow prediction in the WRF-Hydro model with LSTM networks K. Cho & Y. Kim 10.1016/j.jhydrol.2021.127297
80. Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models J. Yang et al. 10.1016/j.ejrh.2024.102034
81. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
82. Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm M. Zakhrouf et al. 10.1080/02723646.2021.1943126
83. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
84. Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems N. Sadiki & D. Jang 10.3390/w16213028
85. Bias learning improves data driven models for streamflow prediction Y. Lin et al. 10.1016/j.ejrh.2023.101557
86. Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology X. Sun et al. 10.3390/su16156598
87. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
88. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
89. Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches A. Husic et al. 10.1016/j.hydroa.2022.100134
90. Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management J. Sun et al. 10.1016/j.jhydrol.2022.127630
91. Machine learning modeling structures and framework for short-term forecasting and long-term projection of Streamflow T. Tran & J. Kim 10.1007/s00477-023-02621-y
92. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
93. A Nonlinear Dynamical Model for Monthly Runoff Forecasting in Situations of Small Samples N. Liu et al. 10.1007/s11004-023-10099-1
94. Long-Term Prediction of Hydrometeorological Time Series Using a PSO-Based Combined Model Composed of EEMD and LSTM G. Wu et al. 10.3390/su151713209
95. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
96. Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales D. Feng et al. 10.1029/2019WR026793
97. Application of temporal convolutional network for flood forecasting Y. Xu et al. 10.2166/nh.2021.021
98. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
99. A Hierarchical Temporal Scale Framework for Data‐Driven Reservoir Release Modeling Q. Longyang & R. Zeng 10.1029/2022WR033922
100. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
101. Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget N. Martin 10.3390/hydrology10060129
102. A self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed Y. Morales et al. 10.1016/j.jhydrol.2020.125910
103. Coupling antecedent rainfall for improving the performance of rainfall thresholds for suspended sediment simulation of semiarid catchments Z. Yin et al. 10.1038/s41598-022-08342-6
104. Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework N. Fernandez et al. 10.1016/j.ejrh.2022.101243
105. Can sampling techniques improve the performance of decomposition-based hydrological prediction models? Exploration of some comparative experiments M. He et al. 10.1007/s13201-022-01696-2
106. Data-driven modeling of municipal water system responses to hydroclimate extremes R. Johnson et al. 10.2166/hydro.2023.170
107. Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network P. Kashyap et al. 10.1109/JSEN.2021.3069266
108. Simulation and attribution analysis based on the long-short-term-memory network for detecting the dominant cause of runoff variation in the Lake Poyang Basin F. Hongxiang et al. 10.18307/2021.0319
109. Streamflow Simulation with High-Resolution WRF Input Variables Based on the CNN-LSTM Hybrid Model and Gamma Test Y. Wang et al. 10.3390/w15071422
110. Internal and external coupling of Gaussian mixture model and deep recurrent network for probabilistic drought forecasting S. Zhu et al. 10.1007/s13762-020-02862-2
111. Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture S. Topp et al. 10.1029/2022WR033880
112. Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel K. Fang & C. Shen 10.1175/JHM-D-19-0169.1
113. A novel operational water quality mobile prediction system with LSTM-Seq2Seq model L. Xie et al. 10.1016/j.envsoft.2024.106290
114. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
115. Data-driven and numerical simulation coupling to quantify the impact of ecological water replenishment on surface water-groundwater interactions K. Lyu et al. 10.1016/j.jhydrol.2024.132508
116. Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation M. Fu et al. 10.1109/ACCESS.2020.2974406
117. Hazard assessment and prediction of ice-jam flooding for a river regulated by reservoirs using an integrated probabilistic modelling approach M. Liu et al. 10.1016/j.jhydrol.2022.128611
118. Study on long short-term memory based on vector direction of flood process for flood forecasting T. Xie et al. 10.1038/s41598-024-72205-5
119. Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning H. Huang et al. 10.3390/rs15163983
120. Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise A. Trucco et al. 10.1109/JOE.2022.3223406
121. Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
122. Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage L. Zou et al. 10.1007/s11269-022-03381-0
123. The Utility of Information Flow in Formulating Discharge Forecast Models: A Case Study From an Arid Snow‐Dominated Catchment C. Tennant et al. 10.1029/2019WR024908
124. Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction D. Kim et al. 10.3390/app12136699
125. Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios Y. Song et al. 10.1016/j.scitotenv.2022.156162
126. Inflow Prediction of Youjiang Reservoir Based on EMD-LSTM model 志. 潘 10.12677/JWRR.2022.111002
127. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
128. Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting X. Le et al. 10.3390/w11071387
129. Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS M. Shen & T. Chui 10.1016/j.jhydrol.2023.130255
130. A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran M. Dasoomi et al. 10.1016/j.treng.2023.100218
131. Convolutional neural network and long short-term memory models for ice-jam predictions F. Madaeni et al. 10.5194/tc-16-1447-2022
132. A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
133. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
134. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
135. Real-time streamflow forecasting: AI vs. Hydrologic insights W. Krajewski et al. 10.1016/j.hydroa.2021.100110
136. An Intelligent Flood Prediction System Using Deep Learning Techniques and Fine Tuned MobileNet Architecture K. Raghu Kumar & R. Biradar 10.1007/s42979-024-02614-w
137. Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators R. Rodriguez-Aguilar et al. 10.3390/math12193124
138. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
139. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
140. A comparative study for predicting lake evaporation at chah nimeh reservoirs in Iran: employing the ADiPLS-LSTM model with an attention mechanism Y. Kheyruri et al. 10.1007/s12145-024-01682-z
141. The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia M. Mohammadi et al. 10.2166/ws.2024.225
142. Comparative Study of Machine Learning and Deep Learning Models Applied to Data Preprocessing Methods for Dam Inflow Prediction Y. Jo & K. Jung 10.22761/GD.2023.0016
143. Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning P. Ai et al. 10.1109/ACCESS.2023.3241995
144. A Bayesian Neural Network for an Accurate Representation and Transformation of Runoff Dynamics: A Case Study of the Brazos River Basin in Texas H. Damavandi et al. 10.12974/2311-8741.2020.08.5
145. Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau C. Zhang et al. 10.1038/s41598-024-84810-5
146. Study on Forecasting Break-Up Date of River Ice in Heilongjiang Province Based on LSTM and CEEMDAN M. Liu et al. 10.3390/w15030496
147. A hydrological process-based neural network model for hourly runoff forecasting S. Gao et al. 10.1016/j.envsoft.2024.106029
148. Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction Y. Wu et al. 10.3390/su14116612
149. How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms D. Kristiyanti et al. 10.1016/j.jjimei.2024.100293
150. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
151. River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India S. Chakraborty & S. Biswas 10.1007/s00477-023-02443-y
152. Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition M. Karbasi et al. 10.1016/j.aej.2023.11.061
153. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al. 10.3389/frwa.2022.886964
154. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
155. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al. 10.5194/acp-24-7041-2024
156. Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning M. Ren et al. 10.1007/s11269-024-03911-y
157. Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model D. Costa Silva et al. 10.3390/en14227707
158. Deep Learning-Based Univariate Prediction of Daily Rainfall: Application to a Flood-Prone, Data-Deficient Country I. Necesito et al. 10.3390/atmos14040632
159. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al. 10.5194/hess-25-2685-2021
160. A surrogate model for the Variable Infiltration Capacity model using deep learning artificial neural network H. Gu et al. 10.1016/j.jhydrol.2020.125019
161. Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling D. Anh et al. 10.1007/s11269-022-03393-w
162. Reconstructing daily streamflow and floods from large-scale atmospheric variables with feed-forward and recurrent neural networks in high latitude climates J. Hagen et al. 10.1080/02626667.2023.2165927
163. Early detection of critical urban events using mobile phone network data P. Lemaire et al. 10.1371/journal.pone.0309093
164. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
165. A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases J. Lu et al. 10.1109/ACCESS.2020.3013543
166. Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework R. Mo et al. 10.1016/j.jhydrol.2023.129736
167. Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
168. A Modified ABCD Model with Temperature-Dependent Parameters for Cold Regions: Application to Reconstruct the Changing Runoff in the Headwater Catchment of the Golmud River, China X. Wang et al. 10.3390/w12061812
169. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al. 10.1016/j.hydres.2023.11.001
170. Auf dem Weg zu besseren Wasserstand-Durchfluss-Beziehungen H. Oppel et al. 10.1007/s35147-023-1879-2
171. Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment G. Gelete 10.1007/s11269-023-03629-3
172. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
173. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
174. Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning M. He et al. 10.3390/w12082088
175. Machine-learning methods for stream water temperature prediction M. Feigl et al. 10.5194/hess-25-2951-2021
176. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
177. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D. Kim et al. 10.3390/w14030466
178. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau B. Li et al. 10.1016/j.jhydrol.2023.129401
179. Performance Improvement of LSTM-based Deep Learning Model for Streamflow Forecasting Using Kalman Filtering F. Bakhshi Ostadkalayeh et al. 10.1007/s11269-023-03492-2
180. A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting Z. Cui et al. 10.2166/nh.2021.016
181. An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting I. Huang et al. 10.1007/s00477-021-02085-y
182. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al. 10.1038/s41598-023-35963-2
183. The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review Z. Sokol et al. 10.3390/rs13030351
184. Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions F. Nogueira Filho et al. 10.3390/w14091318
185. Dynamic flood risk prediction in Houston: a multi-model machine learning approach S. Mishra et al. 10.1080/10106049.2024.2432866
186. Hybrid CNN-LSTM models for river flow prediction X. Li et al. 10.2166/ws.2022.170
187. Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction Y. Yan et al. 10.2166/hydro.2023.006
188. Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing I. Ortiz-Garces et al. 10.3390/app14051737
189. A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region T. Yang et al. 10.1016/j.jhydrol.2021.126723
190. Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin B. Liu et al. 10.3390/w14091429
191. Multiple-model based prediction of weekly discharge of the Brahmaputra-Jamuna by assimilating antecedent hydrological regime M. Rahim et al. 10.1080/10106049.2024.2413551
192. Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation Y. Xu et al. 10.1016/j.jhydrol.2022.127553
193. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
194. Advancing IoT-Based Smart Irrigation R. Togneri et al. 10.1109/IOTM.0001.1900046
195. Prediction of runoff at ungauged areas employing interpolation techniques and deep learning algorithm V. Mahakur et al. 10.1016/j.hydres.2024.12.001
196. Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations L. Jin et al. 10.1016/j.jhydrol.2024.131117
197. Forecasting estuarine salt intrusion in the Rhine–Meuse delta using an LSTM model B. Wullems et al. 10.5194/hess-27-3823-2023
198. Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed C. Tyson et al. 10.1016/j.jhydrol.2023.129304
199. Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques N. Daif & A. Hebal 10.1007/s40808-024-02067-3
200. Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions C. Natel de Moura et al. 10.2166/nh.2022.044
201. Encoding diel hysteresis and the Birch effect in dryland soil respiration models through knowledge-guided deep learning P. Jiang et al. 10.3389/fenvs.2022.1035540
202. Evaluating the Marginal Utility of Two-Stage Hydropower Scheduling W. Ding et al. 10.1061/(ASCE)WR.1943-5452.0001556
203. A coupled model applied to complex river–lake systems Q. Zhang et al. 10.1080/02626667.2023.2285441
204. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
205. Quantifying the Impact of Cascade Reservoirs on Streamflow, Drought, and Flood in the Jinsha River Basin K. Zhang et al. 10.3390/su15064989
206. Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models M. Sawaf et al. 10.1007/s10661-021-09499-9
207. Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting Y. Lian et al. 10.1007/s11269-021-03002-2
208. Streamflow and rainfall forecasting by two long short-term memory-based models L. Ni et al. 10.1016/j.jhydrol.2019.124296
209. APPLICATION OF LONG SHORT-TERM MEMORY (LSTM) NETWORKS APPROACH FOR RIVER WATER LEVEL FORECASTING USING MULTIPLE RIVER BASINS: A CASE STUDY FOR SRI LANKA D. ABEYRATHNE et al. 10.2208/journalofjsce.23-16127
210. Runoff simulation modeling method integrating spatial element dynamics and neural network for remote sensing precipitation data C. Yu et al. 10.1016/j.jhydrol.2024.131875
211. Distinguishing the relative impacts of climate change and anthropogenic activities on variation of water age in the Lake Poyang F. Hongxiang et al. 10.18307/2021.0419
212. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
213. Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data S. Wang & O. Xu 10.1109/ACCESS.2024.3412948
214. Feasibility Study Regarding the Use of a Conformer Model for Rainfall-Runoff Modeling W. Lo et al. 10.3390/w16213125
215. Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream Z. Dong et al. 10.1016/j.jhydrol.2024.130941
216. Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. 10.3389/frwa.2023.1108108
217. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
218. Infrared thermal radiation and deep learning algorithms for evaluating the warm-up effect of sports training: Thermal imaging monitoring model Y. Liu et al. 10.1016/j.tsep.2025.103225
219. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. 10.2166/wst.2024.110
220. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
221. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) A. Wunsch et al. 10.5194/hess-25-1671-2021
222. Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models G. Wei et al. 10.1016/j.jhydrol.2024.130993
223. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity A. Masrur Ahmed et al. 10.1016/j.jhydrol.2021.126350
224. Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes F. Simanjuntak et al. 10.3390/rs14235950
225. An evaluation of statistical and deep learning-based correction of monthly precipitation over the Yangtze River basin in China based on CMIP6 GCMs A. He et al. 10.1007/s10668-024-05005-6
226. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
227. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. 10.1016/j.ejrh.2024.101776
228. A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism Z. Dai et al. 10.3390/w15040670
229. Effects of the spatial and temporal resolution of meteorological data on the accuracy of precipitation estimation by means of CNN T. Nagasato et al. 10.1088/1755-1315/851/1/012033
230. Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models S. Kim et al. 10.1016/j.wroa.2024.100228
231. Application of deep learning in automatic detection of technical and tactical indicators of table tennis F. Qiao & Z. Lv 10.1371/journal.pone.0245259
232. Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks L. An et al. 10.1016/j.jhydrol.2020.125320
233. Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application F. Zeng et al. 10.3390/w12082201
234. Development of an integrated global sensitivity analysis strategy for evaluating process sensitivities across single- and multi-models J. Yang et al. 10.1016/j.jhydrol.2024.132014
235. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
236. Predicting Playa Inundation Using a Long Short‐Term Memory Neural Network K. Solvik et al. 10.1029/2020WR029009
237. Spatiotemporal analysis and prediction of water quality in the Han River by an integrated nonparametric diagnosis approach B. Cheng et al. 10.1016/j.jclepro.2021.129583
238. Modeling abrupt changes in mine water inflow trends: A CEEMDAN-based multi-model prediction approach D. Yao et al. 10.1016/j.jclepro.2024.140809
239. Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco L. El Mezouary et al. 10.1007/s13201-024-02162-x
240. Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method S. Yang et al. 10.1007/s11269-023-03725-4
241. Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions S. Wu et al. 10.1016/j.ecoinf.2024.102914
242. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu 10.1109/ACCESS.2024.3384066
243. EMULATION OF URBAN RUNOFF MODEL BY DEEP LEARNING FOR BENCHMARK VIRTUAL HYETO AND HYDROGRAPH S. FUJIZUKA et al. 10.2208/jscejer.75.I_289
244. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
245. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
246. Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania L. Zhen & A. Bărbulescu 10.3390/w16020289
247. Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia M. Adli Zakaria et al. 10.1016/j.heliyon.2023.e17689
248. Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models A. Pérez-Alarcón et al. 10.1007/s40710-022-00602-x
249. Exploring the best sequence LSTM modeling architecture for flood prediction W. Li et al. 10.1007/s00521-020-05334-3
250. A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation S. Wang et al. 10.2166/nh.2024.035
251. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
252. Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast S. Ahn et al. 10.1016/j.oceaneng.2022.112593
253. A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting Y. Wang et al. 10.1016/j.eswa.2021.115872
254. Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM K. Yokoo et al. 10.1016/j.scitotenv.2021.149876
255. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502
256. Enhancing accuracy of extreme learning machine in predicting river flow using improved reptile search algorithm R. Adnan et al. 10.1007/s00477-023-02435-y
257. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction J. Choi et al. 10.3390/w14182910
258. LSTM with spatiotemporal attention for IoT-based wireless sensor collected hydrological time-series forecasting J. Huang et al. 10.1007/s13042-023-01836-3
259. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
260. Persistent neural calibration for discharges modelling in drought-stressed catchments I. Pulido-Calvo et al. 10.1016/j.eswa.2024.123785
261. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
262. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965
263. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
264. Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India P. Yeditha et al. 10.2166/hydro.2021.067
265. Artificial intelligence applications in the field of streamflow: a bibliometric analysis of recent trends G. Özdoğan Sarıkoç 10.1080/02626667.2024.2356006
266. Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models S. Seshan et al. 10.1016/j.watres.2024.122754
267. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
268. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi 10.1088/1748-9326/ac7247
269. CleverRiver: an open source and free Google Colab toolkit for deep-learning river-flow models M. Luppichini et al. 10.1007/s12145-022-00903-7
270. Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling G. Gelete 10.1007/s12145-023-01041-4
271. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques J. Dharpure et al. 10.1007/s12145-024-01322-6
272. Long lead-time daily and monthly streamflow forecasting using machine learning methods M. Cheng et al. 10.1016/j.jhydrol.2020.125376
273. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
274. Comparison of three recurrent neural networks for rainfall-runoff modelling at a snow-dominated watershed K. Yokoo et al. 10.1088/1755-1315/851/1/012012
275. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
276. Runoff and sediment simulation of terraces and check dams based on underlying surface conditions G. Li et al. 10.1007/s13201-022-01828-8
277. An intelligent procedure for updating deformation prediction of braced excavation in clay using gated recurrent unit neural networks J. Yang et al. 10.1016/j.jrmge.2021.07.011
278. An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System I. Hayder et al. 10.3390/pr11020481
279. Prediction models for urban flood evolution for satellite remote sensing R. Lammers et al. 10.1016/j.jhydrol.2021.127175
280. Deep Learning for Isotope Hydrology: The Application of Long Short-Term Memory to Estimate High Temporal Resolution of the Stable Isotope Concentrations in Stream and Groundwater A. Sahraei et al. 10.3389/frwa.2021.740044
281. Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach P. Li et al. 10.3390/w14060993
282. Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm V. Tran et al. 10.1007/s11269-023-03677-9
283. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis Q. Tian et al. 10.3390/w15183184
284. Assessing different roles of baseflow and surface runoff for long-term streamflow forecasting in southeastern China H. Chen et al. 10.1080/02626667.2021.1988612
285. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
286. Transformer Based Water Level Prediction in Poyang Lake, China J. Xu et al. 10.3390/w15030576
287. Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow P. Sharma et al. 10.1007/s11269-024-03834-8
288. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data A. Ahmed et al. 10.3390/rs13040554
289. Evaluation and prediction of compound geohazards in highly urbanized regions across China's Greater Bay Area K. He et al. 10.1016/j.jclepro.2024.141641
290. Rainfall-runoff modelling – a comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) A. Deulkar et al. 10.1080/09715010.2024.2346244
291. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al. 10.1016/j.envsoft.2023.105730
292. Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables F. Azarpira & S. Shahabi 10.2166/hydro.2021.105
293. A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting A. Khatun et al. 10.1080/02626667.2023.2173012
294. Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin Y. Ouma et al. 10.1007/s40747-021-00365-2
295. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
296. Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks B. Li et al. 10.1007/s11269-022-03133-0
297. The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models Y. Kim et al. 10.1007/s00477-022-02366-0
298. Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments L. Zhong et al. 10.1029/2022WR034118
299. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. 10.1029/2021WR030185
300. Improving Jakarta’s Katulampa Barrage Extreme Water Level Prediction Using Satellite-Based Long Short-Term Memory (LSTM) Neural Networks H. Kardhana et al. 10.3390/w14091469
301. CREATION AND VERIFICATION OF A PRETRAINED MODEL FOR RIVER FLOOD PREDICTIONS N. KIMURA et al. 10.2208/jscejj.23-16147
302. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
303. Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing Y. Yang & T. Chui 10.1029/2020WR029471
304. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
305. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
306. An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records S. Wei & T. Xu 10.1016/j.agwat.2024.109273
307. Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment J. Wu et al. 10.3390/w11071327
308. Deep learning, hydrological processes and the uniqueness of place K. Beven 10.1002/hyp.13805
309. An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network W. Wang et al. 10.1007/s11269-021-02920-5
310. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al. 10.1016/j.jhydrol.2022.128577
311. The prediction model of water level in front of the check gate of the LSTM neural network based on AIW-CLPSO L. Gao et al. 10.1007/s10878-023-01101-x
312. Surface and high-altitude combined rainfall forecasting using convolutional neural network P. Zhang et al. 10.1007/s12083-020-00938-x
313. Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks H. Ren et al. 10.5194/hess-26-1727-2022
314. Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months H. Chu et al. 10.3390/w16040593
315. Construction of a mental health risk model for college students with long and short-term memory networks and early warning indicators Y. Feng et al. 10.1515/jisys-2023-0318
316. A review of machine learning applications to coastal sediment transport and morphodynamics E. Goldstein et al. 10.1016/j.earscirev.2019.04.022
317. A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection M. Al-Alawi et al. 10.1016/j.est.2024.112866
318. EXAMINATION OF SHORT-TERM WATER LEVEL PREDICTION MODEL IN LOW-LYING LAKES USING MACHINE LEARNING M. KIMURA et al. 10.2208/jscejhe.76.2_I_439
319. Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach S. Chen et al. 10.1016/j.jhydrol.2023.129734
320. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
321. Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia S. Clark et al. 10.5194/hess-28-1191-2024
322. Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure Z. Cui et al. 10.1016/j.jhydrol.2022.127764
323. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
324. Water quality prediction based on sparse dataset using enhanced machine learning S. Huang et al. 10.1016/j.ese.2024.100402
325. Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods X. Wang et al. 10.1016/j.ecoinf.2024.102721
326. Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data S. Zhao et al. 10.1016/j.ecoinf.2023.102385
327. Which riverine water quality parameters can be predicted by meteorologically-driven deep learning? S. Huang et al. 10.1016/j.scitotenv.2024.174357
328. Dynamic Loss Balancing and Sequential Enhancement for Road-Safety Assessment and Traffic Scene Classification M. Kačan et al. 10.1109/TITS.2024.3456214
329. Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks F. Alharbi & D. Csala 10.3390/en14206501
330. Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso Y. Song & J. Zhang 10.2166/wst.2024.142
331. A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting S. Khorram & N. Jehbez 10.1007/s11269-023-03541-w
332. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. 10.1016/j.jhydrol.2023.130350
333. A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration J. Chen et al. 10.5194/hess-25-6041-2021
334. Exploring Temporal Dynamics of River Discharge Using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River M. Mehedi et al. 10.3390/hydrology9110202
335. Machine learning models for river flow forecasting in small catchments M. Luppichini et al. 10.1038/s41598-024-78012-2
336. Applications and interpretations of different machine learning models in runoff and sediment discharge simulations J. Miao et al. 10.1016/j.catena.2024.107848
337. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. 10.1016/j.jhydrol.2023.129990
338. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al. 10.1016/j.jhydrol.2023.129474
339. Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis Y. Chen et al. 10.1016/j.jhydrol.2020.125726
340. Physics-Informed Data-Driven Model for Predicting Streamflow: A Case Study of the Voshmgir Basin, Iran P. Parisouj et al. 10.3390/app12157464
341. The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. 10.5194/hess-27-501-2023
342. Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases M. Shoaib et al. 10.1007/s42979-021-00764-9
343. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
344. State Parameter Based Liquefaction Probability Evaluation K. Kumar et al. 10.1007/s40891-023-00495-2
345. Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management H. Afzaal et al. 10.3390/app10051621
346. Prediction of discharge in a tidal river using the LSTM-based sequence-to-sequence models Z. Chen et al. 10.1007/s13131-024-2343-6
347. Enhanced Long Short-Term Memory Model for Runoff Prediction R. Feng et al. 10.1061/(ASCE)HE.1943-5584.0002035
348. Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro 10.1002/hyp.15046
349. Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area Y. Su et al. 10.3390/atmos14091392
350. 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network Y. Kim et al. 10.1109/ACCESS.2022.3179001
351. Daily Streamflow Prediction and Uncertainty Using a Long Short-Term Memory (LSTM) Network Coupled with Bootstrap Z. Wang et al. 10.1007/s11269-022-03264-4
352. User-focused evaluation of National Ecological Observatory Network streamflow estimates S. Rhea et al. 10.1038/s41597-023-01983-w
353. Probabilistic runoff forecasting considering stepwise decomposition framework and external factor integration structure C. Cao et al. 10.1016/j.eswa.2023.121350
354. Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. 10.1029/2021WR031557
355. Application of deep learning to large scale riverine flow velocity estimation M. Forghani et al. 10.1007/s00477-021-01988-0
356. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani 10.1038/s41597-025-04389-y
357. Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance A. Mandal et al. 10.3390/sym13081544
358. Spatial-temporal behavior of precipitation driven karst spring discharge in a mountain terrain X. Song et al. 10.1016/j.jhydrol.2022.128116
359. Developing a deep learning model for the simulation of micro-pollutants in a watershed D. Yun et al. 10.1016/j.jclepro.2021.126858
360. A deep learning model coupled with metaheuristic optimization for urban rainfall prediction W. Zhao et al. 10.1016/j.jhydrol.2024.132596
361. Improving streamflow forecasting in semi-arid basins by combining data segmentation and attention-based deep learning Z. Tang et al. 10.1016/j.jhydrol.2024.131923
362. Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks K. Ishida et al. 10.2166/hydro.2021.095
363. Joint Spatial and Temporal Modeling for Hydrological Prediction Q. Zhao et al. 10.1109/ACCESS.2020.2990181
364. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
365. Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas F. Forghanparast & G. Mohammadi 10.3390/w14192972
366. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
367. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
368. Snowmelt-Driven Streamflow Prediction Using Machine Learning Techniques (LSTM, NARX, GPR, and SVR) S. Thapa et al. 10.3390/w12061734
369. Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China W. Wang et al. 10.3390/w16111589
370. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
371. A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India S. Kumar et al. 10.1038/s41598-024-77655-5
372. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels E. Chen et al. 10.1016/j.envsoft.2024.106072
373. High resolution data visualization and machine learning prediction of free chlorine residual in a green building water system S. Wei et al. 10.1016/j.wroa.2024.100244
374. An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Y. Ma et al. 10.3389/frwa.2021.723548
375. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
376. A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction L. Tao et al. 10.1016/j.jhydrol.2021.126815
377. A Novel Instrument for Bed Dynamics Observation Supports Machine Learning Applications in Mangrove Biogeomorphic Processes Z. Hu et al. 10.1029/2020WR027257
378. Performance of long short-term memory networks in predicting athlete injury risk H. Tao et al. 10.3233/JCM-247563
379. A Comparative Analysis of ANN, LSTM and Hybrid PSO-LSTM Algorithms for Groundwater Level Prediction S. Thakur & S. Karmakar 10.1007/s41403-024-00505-3
380. Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection T. Tran et al. 10.3390/math9050551
381. Forecasting of water quality parameters of Sandia station in Narmada basin, Central India, using AI techniques D. Tiwari et al. 10.2166/wcc.2024.520
382. Prediction of karst spring discharge using LSTM with Bayesian optimisation hyperparameter tuning: a laboratory physical model approach P. Opoku et al. 10.1007/s40808-023-01828-w
383. Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting M. Rahimzad et al. 10.1007/s11269-021-02937-w
384. Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms K. Khosravi et al. 10.1007/s11269-021-03051-7
385. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
386. Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm Y. Jhong et al. 10.1007/s11269-023-03713-8
387. A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring M. Gross et al. 10.1016/j.envsoft.2024.106247
388. Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al. 10.1016/j.jhydrol.2023.130504
389. Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry M. Forghani et al. 10.1016/j.advwatres.2022.104323
390. Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation X. Zhang et al. 10.1162/dint_a_00221
391. Deep learning for earthquake hydrology? Insights from the karst Gran Sasso aquifer in central Italy A. Scorzini et al. 10.1016/j.jhydrol.2022.129002
392. Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation G. Gelete & Z. Yaseen 10.1016/j.jhydrol.2024.131704
393. Simulation of spring discharge using graph neural networks at Niangziguan Springs, China Y. Gai et al. 10.1016/j.jhydrol.2023.130079
394. Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake R. You et al. 10.1016/j.jhydrol.2024.132173
395. Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms Y. Essam et al. 10.1038/s41598-021-04419-w
396. Tide level prediction during typhoons based on variable topology in graph convolution recurrent neural networks X. Shi et al. 10.1016/j.oceaneng.2024.119228
397. Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms Y. Essam et al. 10.1038/s41598-022-07693-4
398. Rainfall forecasting in upper Indus basin using various artificial intelligence techniques M. Hammad et al. 10.1007/s00477-021-02013-0
399. Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations T. Song et al. 10.3390/w12030912
400. Deep insight into daily runoff forecasting based on a CNN-LSTM model H. Deng et al. 10.1007/s11069-022-05363-2
401. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
402. Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season K. Fang et al. 10.3389/frwa.2024.1456647
403. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al. 10.1002/joc.7738
404. Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment L. Vinokić et al. 10.1016/j.wroa.2024.100297
405. WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting I. Demir et al. 10.5194/essd-14-5605-2022
406. Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
407. Numerical modeling the impacts of increasing groundwater pumping upon discharge decline of the BL Spring located in Xilin Gol League in east inner Mongolia, China H. Xiao et al. 10.3389/fenvs.2024.1400569
408. A new seq2seq architecture for hourly runoff prediction using historical rainfall and runoff as input S. Gao et al. 10.1016/j.jhydrol.2022.128099
409. Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia S. Clark 10.1016/j.envsoft.2022.105295
410. Combining Multiple Machine Learning Methods Based on CARS Algorithm to Implement Runoff Simulation Y. Fan et al. 10.3390/w16172397
411. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
412. Short-term runoff prediction using deep learning multi-dimensional ensemble method G. Liu et al. 10.1016/j.jhydrol.2022.127762
413. A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions H. Chu et al. 10.1016/j.ecolind.2023.110092
414. A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings A. Mohanty et al. 10.1016/j.jhydrol.2024.131225
415. Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation W. Li et al. 10.1038/s41598-024-62127-7
416. Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons M. Boucher et al. 10.1029/2019WR026226
417. Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes H. De Gracia et al. 10.3390/w16121662
418. A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning Y. Liu et al. 10.3390/w15091716
419. Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea H. Han et al. 10.2166/ws.2023.012
420. Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions K. Fang et al. 10.1029/2020WR028095
421. Implementing augmented deep Machine learning for effective shallow water table management and forecasting M. Zeynoddin et al. 10.1016/j.jhydrol.2024.132371
422. Water level prediction of Liuxihe Reservoir based on improved long short-term memory neural network Y. Li et al. 10.2166/ws.2023.282
423. Deep dependence in hydroclimatological variables T. Lee & J. Kim 10.1007/s10489-024-05345-w
424. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al. 10.1175/JHM-D-20-0072.1
425. Investigating the ability of deep learning on actual evapotranspiration estimation in the scarcely observed region X. Wang et al. 10.1016/j.jhydrol.2022.127506
426. Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
427. Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques S. Erfani & E. Goharian 10.2166/hydro.2023.146
428. Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. 10.3390/w15050947
429. Streamflow simulation and forecasting using remote sensing and machine learning techniques E. Soo et al. 10.1016/j.asej.2024.103099
430. Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
431. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
432. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
433. Predicting mean annual and mean monthly streamflow in Colorado ungauged basins A. Eurich et al. 10.1002/rra.3778
434. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
435. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
436. A 500-year annual runoff reconstruction for 14 selected European catchments S. Nasreen et al. 10.5194/essd-14-4035-2022
437. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al. 10.3390/rs14102488
438. A critical review of RNN and LSTM variants in hydrological time series predictions M. Waqas & U. Humphries 10.1016/j.mex.2024.102946
439. Comparison of Process-Driven SWAT Model and Data-Driven Machine Learning Techniques in Simulating Streamflow: A Case Study in the Fenhe River Basin Z. Jiang et al. 10.3390/su16146074
440. An Index Used to Evaluate the Applicability of Mid-to-Long-Term Runoff Prediction in a Basin Based on Mutual Information S. Xie et al. 10.3390/w16111619
441. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
442. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
443. Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang 10.3390/w15234194
444. Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions H. Apaydin et al. 10.1016/j.jhydrol.2021.126506
445. Wavelet analysis of rainfall and application of hydrological model in a semi‐arid river basin of Rajasthan, India D. Sharma et al. 10.1002/clen.202300223
446. Simulate the forecast capacity of a complicated water quality model using the long short-term memory approach Z. Liang et al. 10.1016/j.jhydrol.2019.124432
447. Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning H. Weierbach et al. 10.3390/w14071032
448. Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level J. Kim et al. 10.3390/w14091512
449. Comparison of machine learning techniques for reservoir outflow forecasting O. García-Feal et al. 10.5194/nhess-22-3859-2022
450. Research on parameter regionalization based on decision tree algorithm: a case study of 16 catchments in northeast China X. Zhao et al. 10.1080/02626667.2024.2436113
451. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
452. Short-term Lake Erie algal bloom prediction by classification and regression models H. Ai et al. 10.1016/j.watres.2023.119710
453. Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques Y. Chang et al. 10.3390/w15234100
454. Sponge City Drainage System Prediction Based on Artificial Neural Networks: Taking SCRC System as Example Y. Ren et al. 10.3390/w16182587
455. Surrogate optimization of deep neural networks for groundwater predictions J. Müller et al. 10.1007/s10898-020-00912-0
456. Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models P. Bai et al. 10.1016/j.jhydrol.2020.125779
457. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. 10.5194/hess-26-5793-2022
458. Performance of statistical and machine learning ensembles for daily temperature downscaling X. Li et al. 10.1007/s00704-020-03098-3
459. EMULATION EVALUATION OF URBAN RUNOFF MODEL BY DEEP LEARNING FOR THE VIRTUAL HYDROGRAPH WITH OBSERVATION NOISE S. FUJIZUKA et al. 10.2208/jscejer.76.5_I_383
460. Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta Q. Tian et al. 10.3389/fmars.2024.1407690
461. Multilayer self‐attention residual network for code search H. Hu et al. 10.1002/cpe.7650
462. A SOM-LSTM combined model for groundwater level prediction in karst critical zone aquifers considering connectivity characteristics F. Guo et al. 10.1007/s12665-024-11567-5
463. Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory Y. Lian et al. 10.1007/s11269-022-03097-1
464. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
465. Intercomparing LSTM and RNN to a Conceptual Hydrological Model for a Low-Land River with a Focus on the Flow Duration Curve A. Ley et al. 10.3390/w15030505
466. Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data R. Adnan et al. 10.1007/s00704-023-04624-9
467. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
468. Deep Lagged-Wavelet for monthly rainfall forecasting in a tropical region E. Vivas et al. 10.1007/s00477-022-02323-x
469. Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India B. Supreetha et al. 10.1155/2020/8685724
470. Evaluating the performance of bias-corrected IMERG satellite rainfall estimates for hydrological simulation over the Upper Bhima River basin, India S. Nandi & M. Janga Reddy 10.1080/10106049.2022.2101695
471. Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area S. Yang et al. 10.1007/s11069-022-05766-1
472. Hybrid short-term runoff prediction model based on optimal variational mode decomposition, improved Harris hawks algorithm and long short-term memory network W. Sun et al. 10.1088/2515-7620/ac5feb
473. Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models C. Sezen & M. Šraj 10.1016/j.scitotenv.2024.171684
474. A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting N. Fouotsa Manfouo et al. 10.3390/atmos14040708
475. A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. 10.2166/nh.2024.016
476. Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths Y. Zhou et al. 10.1029/2022WR033214
477. Combining signal decomposition and deep learning model to predict noisy runoff coefficient A. Rahi et al. 10.1016/j.jhydrol.2024.131815
478. In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM T. Zhang et al. 10.3390/rs15102693
479. Bias correction of the hourly satellite precipitation product using machine learning methods enhanced with high-resolution WRF meteorological simulations N. Yao et al. 10.1016/j.atmosres.2024.107637
480. Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management A. Essenfelder et al. 10.3390/atmos11121305
481. Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects M. Zounemat-Kermani et al. 10.1016/j.jhydrol.2020.125085
482. Forecasting Multi-Step-Ahead Street-Scale nuisance flooding using seq2seq LSTM surrogate model for Real-Time applications in a Coastal-Urban city B. Roy et al. 10.1016/j.jhydrol.2025.132697
483. Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea Y. Kwon et al. 10.1038/s41598-023-36439-z
484. Integrated framework for hydrologic modelling in data-sparse watersheds and climate change impact on projected green and blue water sustainability I. Lawal et al. 10.3389/fenvs.2023.1233216
485. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
486. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
487. A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada M. Bourget et al. 10.1017/asb.2024.19
488. Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis R. Costache et al. 10.1016/j.jhydrol.2022.127747
489. Flash Flood Forecasting Based on Long Short-Term Memory Networks T. Song et al. 10.3390/w12010109
490. A coupled model of nonlinear dynamical system and deep learning for multi-step ahead daily runoff prediction for data scarce regions L. Qian et al. 10.1016/j.jhydrol.2024.132640
491. Estimation of time-varying parameter in Budyko framework using long short-term memory network over the Loess Plateau, China F. Wang et al. 10.1016/j.jhydrol.2022.127571
492. An Approach Based on Recurrent Neural Networks and Interactive Visualization to Improve Explainability in AI Systems W. Villegas-Ch et al. 10.3390/bdcc7030136
493. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions? Y. Khoshkalam et al. 10.1016/j.jhydrol.2024.132540
494. Machine learning-based techniques for land subsidence simulation in an urban area J. Liu et al. 10.1016/j.jenvman.2024.120078
495. Alkali-activated binder concrete strength prediction using hybrid-deep learning along with shapely additive explanations and uncertainty analysis P. Das et al. 10.1016/j.conbuildmat.2024.136711
496. Large-scale flash flood warning in China using deep learning G. Zhao et al. 10.1016/j.jhydrol.2021.127222
497. Improving Deep Learning Forecasting Model Based on LSTM for Türkiye’s Hydro-Electricity Generation M. Bulut 10.35377/saucis...1503018
498. On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation H. Guo et al. 10.1016/j.ecolmodel.2022.110250
499. Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin D. Hughes et al. 10.1016/j.ejrh.2023.101482
500. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction B. Alizadeh et al. 10.1016/j.jhydrol.2021.126526
501. Neural Structures to Predict River Stages in Heavily Urbanized Catchments A. Chiacchiera et al. 10.3390/w14152330
502. Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany Y. Liu et al. 10.1016/j.jhydrol.2024.132395
503. Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models K. Calcraft et al. 10.1016/j.envsoft.2024.106236
504. Comparison of Deep Learning Techniques for River Streamflow Forecasting X. Le et al. 10.1109/ACCESS.2021.3077703
505. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
506. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
507. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
508. Development of a water quality prediction model using ensemble empirical mode decomposition and long short-term memory S. Yoon et al. 10.5004/dwt.2023.29771
509. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al. 10.1007/s00521-022-07523-8
510. Reconstruction of groundwater level at Kumamoto, Japan by means of deep learning to evaluate its increase by the 2016 earthquake K. Yokoo et al. 10.1088/1755-1315/851/1/012032
511. Simulating sediment discharge at water treatment plants under different land use scenarios using cascade modelling with an expert-based erosion-runoff model and a deep neural network E. Patault et al. 10.5194/hess-25-6223-2021
512. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
513. A computationally efficient flash flood early warning system for a mountainous and transboundary river basin in Bangladesh N. Biswas et al. 10.2166/hydro.2020.202
514. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al. 10.1016/j.jenvman.2024.120931
515. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
516. Multi-temporal scale analysis of complementarity between hydro and solar power along an alpine transect T. Pérez Ciria et al. 10.1016/j.scitotenv.2020.140179
517. Establishing hybrid deep learning models for regional daily rainfall time series forecasting in the United Kingdom G. Thottungal Harilal et al. 10.1016/j.engappai.2024.108581
518. A high-efficiency spaceborne processor for hybrid neural networks S. Wang et al. 10.1016/j.neucom.2023.126230
519. Daily soil temperature simulation at different depths in the Red River Basin: a long short-term memory approach M. Tahmasebi Nasab et al. 10.1007/s40808-024-01988-3
520. Improved runoff forecasting performance through error predictions using a deep-learning approach H. Han & R. Morrison 10.1016/j.jhydrol.2022.127653
521. Machine learning for predicting discharge fluctuation of a karst spring in North China S. Cheng et al. 10.1007/s11600-020-00522-0
522. Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example G. Wang et al. 10.3390/rs15041118
523. Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique M. Behbahani et al. 10.1007/s00477-024-02788-y
524. Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau Q. Yu et al. 10.1016/j.jhydrol.2023.129115
525. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
526. Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. 10.1021/acs.est.2c02232
527. Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa P. Omonge et al. 10.1016/j.ejrh.2021.100983
528. Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile N. Flores et al. 10.3390/w13223191
529. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
530. Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model Q. Cao et al. 10.1111/jfr3.12827
531. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
532. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
533. Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds Y. Zhu et al. 10.1109/ACCESS.2020.2971264
534. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso 10.18034/ajase.v8i1.10
535. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
536. Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data L. Qian et al. 10.1016/j.jhydrol.2024.131755
537. Impact of Input Feature Selection on Groundwater Level Prediction From a Multi-Layer Perceptron Neural Network R. Sahu et al. 10.3389/frwa.2020.573034
538. Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh 10.3390/w15061036
539. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
540. Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion A. Ahmed Osman et al. 10.1016/j.gsd.2024.101152
541. A new method for predicting precipitation δ 18 O distribution based on deep learning and spatio-temporal clustering Y. Li et al. 10.1080/02626667.2024.2375403
542. Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al. 10.1002/hyp.15270
543. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
544. Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin Z. Mei et al. 10.1007/s11269-024-03975-w
545. Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area L. Bian et al. 10.1016/j.jhydrol.2023.130091
546. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
547. Runoff forecasting model based on variational mode decomposition and artificial neural networks X. Jing et al. 10.3934/mbe.2022076
548. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
549. AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM C. Yang et al. 10.1109/ACCESS.2022.3154812
550. Evolution of gas kick and overflow in wellbore and formation pressure inversion method under the condition of failure in well shut-in during a blowout G. Ju et al. 10.1016/j.petsci.2022.01.004
551. Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes E. Orland et al. 10.1029/2020GL088731
552. A review of Earth Artificial Intelligence Z. Sun et al. 10.1016/j.cageo.2022.105034
553. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
554. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
555. Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al. 10.3390/ijgi12110464
556. Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS T. Kim et al. 10.1016/j.jhydrol.2021.126423
557. On the use of convolutional deep learning to predict shoreline change E. Gomez-de la Peña et al. 10.5194/esurf-11-1145-2023
558. Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach N. Kimura et al. 10.3390/w13081109
559. Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States K. Khand & G. Senay 10.1016/j.mlwa.2024.100551
560. Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al. 10.1007/s11356-024-33594-2
561. Impact of training data size on the LSTM performances for rainfall–runoff modeling T. Boulmaiz et al. 10.1007/s40808-020-00830-w
562. An improved long short-term memory network for streamflow forecasting in the upper Yangtze River S. Zhu et al. 10.1007/s00477-020-01766-4
563. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
564. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. 10.3390/w14111745
565. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
566. Quantitative study of rainfall lag effects and integration of machine learning methods for groundwater level prediction modelling Y. Wang et al. 10.1002/hyp.15171
567. Research on Precipitation Forecast Based on LSTM–CP Combined Model Y. Guo et al. 10.3390/su132111596
568. Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting I. Kao et al. 10.1016/j.jhydrol.2020.124631
569. A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction S. Senanayake et al. 10.1016/j.scitotenv.2022.157220
570. Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting A. Barrera-Animas et al. 10.1016/j.mlwa.2021.100204
571. Newton method–GR1 coupling to model rainfall–runoff relationship: case study—Boumessaoud basin (NO of Algeria) and Seine basin (NO of France) O. Noureddine et al. 10.1007/s40808-022-01373-y
572. Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
573. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al. 10.1007/s11356-021-17084-3
574. Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S. Liu et al. 10.3389/frwa.2023.1150126
575. A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils J. Zhou et al. 10.3390/s23125599
576. Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision T. Hascoet et al. 10.3390/rs16010170
577. Streamflow modeling in the Colombian Andes: insights from watersheds with diverse physical properties and climate patterns M. Alvarez & J. Barco 10.1080/23249676.2024.2443743
578. Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation H. Lin et al. 10.3390/w16162346
579. Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea J. Choi et al. 10.1016/j.ecoleng.2022.106699
580. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China H. Tian et al. 10.1016/j.agrformet.2021.108629
581. Ứng dụng mô hình đa biến bộ nhớ dài - ngắn hạn trong dự báo nhiệt độ và lượng mưa T. Dương & T. Nguyễn 10.22144/ctu.jvn.2022.158
582. Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing S. Wang et al. 10.1051/jnwpu/20213930510
583. Three different models to evaluate water discharge: An application to a river section at Vinh Tuy location in the Lo river basin, Vietnam C. Pham Van & G. Nguyen–Van 10.1016/j.jher.2021.12.002
584. Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective Y. Zhaxi et al. 10.3389/fpsyg.2022.804447
585. Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
586. Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Y. Ma et al. 10.5194/hess-25-3555-2021
587. Estimating groundwater recharge across Africa during 2003–2023 using GRACE-derived groundwater storage changes V. Ferreira et al. 10.1016/j.ejrh.2024.102046
588. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
589. Forecast of rainfall distribution based on fixed sliding window long short-term memory C. Chen et al. 10.1080/19942060.2021.2009374
590. Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia Y. Sudriani et al. 10.1088/1755-1315/299/1/012037
591. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers W. Zhi et al. 10.1038/s44221-023-00038-z
592. Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach S. Fleming et al. 10.1016/j.jhydrol.2021.126327
593. Machine learning-based surrogate model assisting stochastic model predictive control of urban drainage systems X. Luo et al. 10.1016/j.jenvman.2023.118974
594. Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
595. Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data J. Jeong et al. 10.1016/j.jhydrol.2019.124512
596. CDLSTM: A Novel Model for Climate Change Forecasting M. Anul Haq 10.32604/cmc.2022.023059
597. An Improved Anticipated Learning Machine for Daily Runoff Prediction in Data-Scarce Regions W. Hu et al. 10.1007/s11004-024-10154-5
598. Analysis of rainfall and temperature using deep learning model S. Choudhary & S. Ghosh 10.1007/s00704-023-04493-2
599. Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting T. Tuğrul et al. 10.1007/s12145-024-01541-x
600. Soil erosion sensitivity and prediction for hilly areas of Hubei Province, China, using combined RUSLE and LSTM models Y. Ping et al. 10.1007/s11368-023-03668-8
601. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
602. Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks S. He et al. 10.1007/s11269-022-03401-z
603. A probabilistic integration of LSTM and Gaussian process regression for uncertainty-aware reservoir water level predictions K. Tandon & S. Sen 10.1080/02626667.2024.2428428
604. Hierarchical prediction of soil water content time series F. Leij et al. 10.1016/j.catena.2021.105841
605. Enhanced machine learning model via twin support vector regression for streamflow time series forecasting of hydropower reservoir X. Fu et al. 10.1016/j.egyr.2023.09.071
606. Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches F. Ahmadi et al. 10.1007/s13201-023-01943-0
607. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
608. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. 10.5194/hess-23-5089-2019
609. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
610. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
611. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
612. SUPPORT SYSTEM FOR SPECULATION BY EXCHANGE TRADES FUNDS G. Tumaševičius & N. Maknickienė 10.3846/mla.2022.15870
613. Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al. 10.1016/j.jhydrol.2023.129688
614. Application of SA-Conv1D-BiGRU model for streamflow prediction in southern Ethiopia N. Mena 10.2166/nh.2024.074
615. The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data S. Lu et al. 10.1038/s41598-024-67611-8
616. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al. 10.1016/j.envsoft.2021.105112
617. A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction K. Johny et al. 10.1016/j.asoc.2022.108941
618. Solving flood problems with deep learning technology: Research status, strategies, and future directions H. Li et al. 10.1002/sd.3074
619. Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China F. Wang et al. 10.3390/rs13050889
620. Flood Prediction and Uncertainty Estimation Using Deep Learning V. Gude et al. 10.3390/w12030884
621. Simulating block-scale flood inundation and streamflow using the WRF-Hydro model in the New York City metropolitan area B. Kilicarslan & M. Temimi 10.1007/s11069-024-06597-y
622. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks A. Abbas et al. 10.1016/j.jhydrol.2020.125370
623. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting V. Moreido et al. 10.3390/w13121696
624. Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation N. Martin & J. White 10.3390/w16192758
625. Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review A. Akinsoji et al. 10.1007/s11269-024-03885-x
626. Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts I. Kao et al. 10.1016/j.jhydrol.2021.126371
627. Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique W. Achini Ishankha et al. 10.1016/j.jenvman.2024.123082
628. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model R. Barzegar et al. 10.1007/s00477-020-01776-2
629. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
630. Runoff prediction of urban stream based on the discharge of pump stations using improved multi-layer perceptron applying new optimizers combined with a harmony search E. Lee 10.1016/j.jhydrol.2022.128708
631. High temporal resolution urban flood prediction using attention-based LSTM models L. Zhang et al. 10.1016/j.jhydrol.2023.129499
632. Modeling injection-induced fault slip using long short-term memory networks U. Mital et al. 10.1016/j.jrmge.2024.09.006
633. Predicting stock market index using LSTM H. Bhandari et al. 10.1016/j.mlwa.2022.100320
634. Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation H. Han et al. 10.3390/w13040437
635. Machine learning models to complete rainfall time series databases affected by missing or anomalous data A. Lupi et al. 10.1007/s12145-023-01122-4
636. Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning J. Liu et al. 10.5194/hess-26-265-2022
637. Deep learning convolutional neural network in rainfall–runoff modelling S. Van et al. 10.2166/hydro.2020.095
638. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
639. Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management S. Yang et al. 10.1016/j.scitotenv.2023.162056
640. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
641. Towards Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning A. Sanjay Potdar et al. 10.1175/JHM-D-20-0302.1
642. Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks K. Leskovar et al. 10.3390/app11167317
643. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
644. Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series W. Wang et al. 10.1007/s11269-024-03806-y
645. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
646. Comparison of Multilayer Perceptron with an Optimal Activation Function and Long Short-Term Memory for Rainfall-Runoff Simulations and Ungauged Catchment Runoff Prediction M. Shin & Y. Jung 10.1007/s11269-024-04074-6
647. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
648. A Novel Methodology for Predicting the Production of Horizontal CSS Wells in Offshore Heavy Oil Reservoirs Using Particle Swarm Optimized Neural Network L. Zhang et al. 10.3390/app13042540
649. Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts K. Bogner et al. 10.3390/su11123328
650. Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay J. Lee et al. 10.1016/j.jhydrol.2022.128916
651. Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea J. Kwak et al. 10.1007/s00477-021-02094-x
652. Assessing the power of non-parametric data-driven approaches to analyse the impact of drought measures J. De Meester & P. Willems 10.1016/j.envsoft.2023.105923
653. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
654. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
655. Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data D. Feng et al. 10.1029/2021GL092999
656. Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation R. Zhou & Y. Zhang 10.1007/s11356-022-21597-w
657. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. 10.1016/j.xinn.2024.100617
658. A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework Z. Xu et al. 10.3390/w13233390
659. Long-term prediction of runoff of Heiher River in China based on EMD-LSTM networks H. Xue et al. 10.1051/matecconf/202439501005
660. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
661. Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model L. Ni et al. 10.1016/j.jhydrol.2020.124901
662. Daily runoff forecasting based on data-augmented neural network model X. Bi et al. 10.2166/hydro.2020.017
663. Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches A. Gogineni et al. 10.1007/s12145-024-01397-1
664. Evaluation of Data-Driven and Process-Based Real-Time Flow Forecasting Techniques for Informing Operation of Surface Water Abstraction M. Yassin et al. 10.1061/(ASCE)WR.1943-5452.0001397
665. Crop yield prediction based on reanalysis and crop phenology data in the agroclimatic zones S. Yeşilköy & I. Demir 10.1007/s00704-024-05046-x
666. Incorporating hydrological constraints with deep learning for streamflow prediction Y. Zhou et al. 10.1016/j.eswa.2024.125379
667. Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data C. Yang et al. 10.3390/math10162936
668. Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
669. Optimizing seasonal discharge predictions: a hybridized approach with AI and non-linear models S. Sharma & M. Patel 10.1007/s41939-024-00401-x
670. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
671. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
672. Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm S. Kumar et al. 10.2166/hydro.2022.009
673. Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting G. Zuo et al. 10.1016/j.jhydrol.2020.124776
674. Daily Runoff Prediction Based on FA-LSTM Model Q. Chai et al. 10.3390/w16162216
675. Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry S. Hubbard et al. 10.1002/hyp.13807
676. An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model S. Moghadam et al. 10.1007/s10661-021-09586-x
677. Evaluation of Transformer model and Self-Attention mechanism in the Yangtze River basin runoff prediction X. Wei et al. 10.1016/j.ejrh.2023.101438
678. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information L. Xu et al. 10.1016/j.jhydrol.2024.131230
679. Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater M. Khan et al. 10.1016/j.ijbiomac.2024.134701
680. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
681. Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment V. Kumar et al. 10.3934/environsci.2025004
682. Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation H. Gui et al. 10.1002/clen.202000381
683. Reservoir-based flood forecasting and warning: deep learning versus machine learning S. Yi & J. Yi 10.1007/s13201-024-02298-w
684. The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff G. Ayzel et al. 10.1080/02626667.2020.1762886
685. Forecasting the potential of reclaimed water using signal decomposition and deep learning Y. Chen et al. 10.1016/j.jwpe.2024.105770
686. Urban flood forecasting using a hybrid modeling approach based on a deep learning technique H. Moon et al. 10.2166/hydro.2023.203
687. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
688. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
689. A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data W. Ming et al. 10.3390/rs14071744
690. Applications of machine learning to water resources management: A review of present status and future opportunities A. Ahmed et al. 10.1016/j.jclepro.2024.140715
691. A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones Y. Wang et al. 10.1029/2023WR035194
692. Forecasting of monthly precipitation based on ensemble empirical mode decomposition and Bayesian model averaging S. Luo et al. 10.3389/feart.2022.926067
693. Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain Z. Liang et al. 10.3389/fenvs.2021.780434
694. Deep Convolutional LSTM for improved flash flood prediction P. Oddo et al. 10.3389/frwa.2024.1346104
695. A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka K. Kurugama et al. 10.1111/jfr3.12980
696. A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India P. Shekar et al. 10.2166/aqua.2023.048
697. Comparative applications of data-driven models representing water table fluctuations J. Jeong & E. Park 10.1016/j.jhydrol.2019.02.051
698. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
699. Development of the Method for Flood Water Level Forecasting and Flood Damage Warning Using an AI-based Model D. Kim et al. 10.9798/KOSHAM.2022.22.4.145
700. Using SARIMA–CNN–LSTM approach to forecast daily tourism demand K. He et al. 10.1016/j.jhtm.2021.08.022
701. Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction H. Choi et al. 10.3390/w14121878
702. Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management L. Zhang et al. 10.1016/j.envres.2024.118267
703. Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations M. ElSaadani et al. 10.3389/frai.2021.636234
704. Improving reservoir inflow prediction via rolling window and deep learning-based multi-model approach: case study from Ermenek Dam, Turkey H. Feizi et al. 10.1007/s00477-022-02185-3
705. On the use of machine learning to account for reservoir management rules and predict streamflow A. Tounsi et al. 10.1007/s00521-022-07500-1
706. Streamflow Predictions in Ungauged Basins Using Recurrent Neural Network and Decision Tree-Based Algorithm: Application to the Southern Region of the Korean Peninsula J. Won et al. 10.3390/w15132485
707. Post-processing of the UKMO ensemble precipitation product over various regions of Iran: integration of long short-term memory model with principal component analysis S. Alizadeh et al. 10.1007/s00704-022-04170-w
708. Extreme pressure coefficients: modelling a hydraulic jump using deep-learning based methods S. Mousavi et al. 10.1007/s12046-024-02515-x
709. Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning S. Wang et al. 10.1016/j.watres.2024.122614
710. Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation V. Sessa et al. 10.3390/cleantechnol3040050
711. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
712. Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system Y. Li et al. 10.1016/j.ese.2023.100320
713. Leveraging neural network models to improve boundary condition inputs for the CE-QUAL-W2 model in reservoir turbidity simulations S. Kim & S. Chung 10.1016/j.ejrh.2024.102064
714. Exploring deep learning capabilities for surge predictions in coastal areas T. Tiggeloven et al. 10.1038/s41598-021-96674-0
715. Applicability of recurrent neural networks to retrieve missing runoff records: challenges and opportunities in Turkey Y. Alsavaf & A. Teksoy 10.1007/s10661-021-09681-z
716. An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach X. Wang et al. 10.1080/02626667.2021.1937631
717. Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
718. River Flooding Forecasting and Anomaly Detection Based on Deep Learning S. Miau & W. Hung 10.1109/ACCESS.2020.3034875
719. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
720. Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction Z. Li et al. 10.3390/w13040575
721. A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting R. Zhou et al. 10.1016/j.jhydrol.2024.131128
722. Understanding the Way Machines Simulate Hydrological Processes—A Case Study of Predicting Fine-Scale Watershed Response on a Distributed Framework D. Kim et al. 10.1109/TGRS.2023.3285540
723. An efficient LSTM network for predicting the tailing and multi-peaked breakthrough curves J. Niu et al. 10.1016/j.jhydrol.2023.129914
724. Prediction of Cloud Fractional Cover Using Machine Learning H. Svennevik et al. 10.3390/bdcc5040062
725. Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network R. Kilsdonk et al. 10.3390/hydrology9060105
726. Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal S. Thapa et al. 10.1007/s10661-021-09197-6
727. A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs V. Ngoc Tran et al. 10.1016/j.jhydrol.2024.130608
728. Simulation of Pollution Load at Basin Scale Based on LSTM-BP Spatiotemporal Combination Model L. Li et al. 10.3390/w13040516
729. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
730. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
731. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
732. Research on Runoff Simulations Using Deep-Learning Methods Y. Liu et al. 10.3390/su13031336
733. An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks L. Windheuser et al. 10.1029/2022EA002385
734. Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks A. van Duynhoven & S. Dragićević 10.3390/rs11232784
735. Neural Network Emulation of the Formation of Organic Aerosols Based on the Explicit GECKO‐A Chemistry Model J. Schreck et al. 10.1029/2021MS002974
736. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
737. Prediction of groundwater level under the influence of groundwater exploitation using a data-driven method with the combination of time series analysis and long short-term memory: a case study of a coastal aquifer in Rizhao City, Northern China B. Guo et al. 10.3389/fenvs.2023.1253949
738. Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models G. Li et al. 10.1016/j.jclepro.2024.141228
739. A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
740. Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
741. Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review M. Wang et al. 10.3390/info15080507
742. Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model S. Guniganti et al. 10.1080/02626667.2024.2335294
743. Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model S. Huang et al. 10.3390/w15091704
744. Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
745. Comprehensive comparison of LSTM and VIC model in river ecohydrological regimes alteration attribution: A case study in Laohahe basin, China L. Zhou et al. 10.1016/j.ejrh.2024.101722
746. Enhancing Hydrological Variable Prediction through Multitask LSTM Models Y. Yan et al. 10.3390/w16152156
747. A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework J. Han et al. 10.2166/hydro.2024.244
748. High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation M. Jeong et al. 10.1016/j.jhydrol.2024.131304
749. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
750. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
751. Deep Learning for Time Series Forecasting: Advances and Open Problems A. Casolaro et al. 10.3390/info14110598
752. Forecasting Floods Using Deep Learning Models: A Longitudinal Case Study of Chenab River, Pakistan K. Aatif et al. 10.1109/ACCESS.2024.3445586
753. A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop K. Dolaptsis et al. 10.3390/agriculture14020210
754. Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting T. Tran & J. Kim 10.1007/s00477-024-02776-2
755. Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning H. Kang et al. 10.1007/s12555-019-0984-6
756. Using long short-term memory networks for river flow prediction W. Xu et al. 10.2166/nh.2020.026
757. A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers G. Li et al. 10.1016/j.jhydrol.2023.130025
758. Short- and mid-term forecasts of actual evapotranspiration with deep learning E. Babaeian et al. 10.1016/j.jhydrol.2022.128078
759. Evaluation of Snowmelt Estimation Techniques for Enhanced Spring Peak Flow Prediction J. Agnihotri & P. Coulibaly 10.3390/w12051290
760. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
761. Comparison of the performance of a hydrologic model and a deep learning technique for rainfall- runoff analysis C. Kim & C. Kim 10.1016/j.tcrr.2021.12.001
762. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
763. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
764. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al. 10.1016/j.ejrh.2024.102062
765. Real-time rainfall projection in hilly areas using ARLSTM model: a case of Dharamshala, India D. Mahajan et al. 10.1088/2631-8695/ad9c17
766. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
767. Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds H. Ji et al. 10.1007/s40333-021-0066-5
768. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
769. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
770. Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Y. Man et al. 10.1016/j.eng.2021.12.022
771. Assessing the new Natural Resources Conservation Service water supply forecast model for the American West: A challenging test of explainable, automated, ensemble artificial intelligence S. Fleming et al. 10.1016/j.jhydrol.2021.126782
772. Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm T. Kusudo et al. 10.3390/w14010055
773. A simplified modeling approach for optimization of urban river systems W. Feng et al. 10.1016/j.jhydrol.2023.129689
774. Comparison and interpretation of data-driven models for simulating site-specific human-impacted groundwater dynamics in the North China Plain H. Jing et al. 10.1016/j.jhydrol.2022.128751
775. Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models X. Li et al. 10.3390/su141811149
776. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
777. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
778. Estimating river discharge from rainfall satellite data through simple statistical models P. Birocchi et al. 10.1007/s00704-023-04459-4
779. A hybrid deep learning algorithm and its application to streamflow prediction Y. Lin et al. 10.1016/j.jhydrol.2021.126636
780. Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost G. Gorski et al. 10.1002/lno.12549
781. Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. 10.1016/j.ejrh.2023.101471
782. Forecasting the River Water Discharge by Artificial Intelligence Methods A. Bărbulescu & L. Zhen 10.3390/w16091248
783. Simulation of Rainfall-Runoff process using SWAT model in Bouhamdane watershed, Algeria B. Abdelkebir et al. 10.2298/GSGD2302279A
784. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
785. Dry-Season Water Level Shift Induced by Channel Change of the River–Lake System in China’s Largest Freshwater Lake, Poyang Lake Y. Guo et al. 10.1007/s13157-022-01615-w
786. Comparative analysis of long short-term memory and storage function model for flood water level forecasting of Bokha stream in NamHan River, Korea D. Kim et al. 10.1016/j.jhydrol.2021.127415
787. Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model Y. Zhang et al. 10.1016/j.jclepro.2022.131724
788. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability C. Song 10.1016/j.jhydrol.2021.127324
789. Comparative Analysis of Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques U. Thapa et al. 10.3390/w16152095
790. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
791. On the role of the architecture for spring discharge prediction with deep learning approaches R. Zhou & Y. Zhang 10.1002/hyp.14737
792. The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China M. Liu et al. 10.3390/w12020440
793. Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features Z. Liu et al. 10.1016/j.compenvurbsys.2024.102096
794. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
795. Estimating the influence of water control infrastructure on natural low flow in complex reservoir systems: A case study of the Ohio River G. Atreya et al. 10.1016/j.ejrh.2024.101897
796. Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
797. A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data S. Yang et al. 10.1016/j.jhydrol.2020.125206
798. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
799. Real-time integrated water availability – Salt intrusion modelling and management during droughts D. Bertels et al. 10.1016/j.jhydrol.2024.131894
800. Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting H. Apaydin et al. 10.3390/w12051500
801. A machine learning-based approach for generating high-resolution soil moisture from SMAP products Y. Zhang et al. 10.1080/10106049.2022.2105406
802. Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan C. Hsu & Y. Lin 10.1016/j.agwat.2024.109178
803. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
804. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
805. Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. 10.1007/s10661-023-11649-0
806. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
807. Lake Level Prediction using Feed Forward and Recurrent Neural Networks B. Hrnjica & O. Bonacci 10.1007/s11269-019-02255-2
808. Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling A. Piotrowski et al. 10.1016/j.earscirev.2019.103076
809. A weights combined model for middle and long-term streamflow forecasts and its value to hydropower maximization Y. Guo et al. 10.1016/j.jhydrol.2021.126794
810. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning F. Azad et al. 10.1145/3672556
811. Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River Y. Liu et al. 10.1007/s12205-023-2469-7
812. A parsimonious setup for streamflow forecasting using CNN-LSTM S. Pokharel & T. Roy 10.2166/hydro.2024.114
813. Quantification of the meteorological and hydrological droughts links over various regions of Iran using gridded datasets Y. Kheyruri et al. 10.1007/s11356-023-27498-w
814. Deep learning-based streamflow prediction for western Himalayan river basins T. Majeed et al. 10.1007/s13198-024-02403-x
815. Performance evaluation of ML techniques in hydrologic studies: Comparing streamflow simulated by SWAT, GR4J, and state-of-the-art ML-based models S. Barbhuiya et al. 10.1007/s12040-024-02340-0
816. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
817. Modelling the effect of cascade reservoir regulation on ice-jam flooding M. Liu et al. 10.1016/j.jhydrol.2024.131358
818. Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction Y. Xu et al. 10.1007/s11269-022-03346-3
819. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
820. Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction H. Wang et al. 10.1016/j.ejrh.2024.102120
821. Reconstruction of GRACE Total Water Storage Through Automated Machine Learning A. Sun et al. 10.1029/2020WR028666
822. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
823. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios A. Ahmed et al. 10.1007/s00477-021-01969-3
824. ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data P. Hosseinzadeh et al. 10.3390/hydrology10020029
825. A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network M. Sibtain et al. 10.1155/2020/8828664
826. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
827. Estimation of the daily flow in river basins using the data-driven model and traditional approaches: an application in the Hieu river basin, Vietnam C. Pham Van & H. Le 10.2166/wpt.2022.166
828. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
829. Predicting the peak flow and assessing the hydrologic hazard of the Kessem Dam, Ethiopia using machine learning and risk management centre-reservoir frequency analysis software E. Ayele et al. 10.2166/wcc.2024.320
830. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
831. UTILIZING DEEP LEARNING MODELS IN KABIRDHAM, CHHATTISGARH, TO FORECAST AND MODEL RAINFALL J. Kaushik et al. 10.29121/shodhkosh.v5.i6.2024.2645
832. Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models A. Bennett & B. Nijssen 10.1029/2020WR029328
833. A deep learning model to predict lower temperatures in agriculture M. Guillén-Navarro et al. 10.3233/AIS-200546
834. Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning M. Guillén et al. 10.1007/s11227-020-03288-w
835. Interpreting runoff forecasting of long short-term memory network: An investigation using the integrated gradient method on runoff data from the Han River Basin X. Jing et al. 10.1016/j.ejrh.2023.101549
836. A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning Z. Xiang et al. 10.1029/2019WR025326
837. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C. Shen et al. 10.5194/hess-22-5639-2018
838. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
839. Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation C. Hu et al. 10.3390/w10111543
840. Applications of Deep Learning Models for Forecasting and Modelling Rainwater in Moscow A. Ramadhan et al. 10.1051/bioconf/20249700126
841. Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting Y. Tian et al. 10.3390/w10111655
842. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen 10.1029/2018WR022643