725 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. Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. 10.1016/j.jhydrol.2020.125605
6. 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
7. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
8. 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
9. An active learning convolutional neural network for predicting river flow in a human impacted system S. Reed 10.3389/frwa.2023.1271780
10. 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
11. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
12. 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
13. A gradient-enhanced sequential nonparametric data assimilation framework for soil moisture flow Y. Wang et al. 10.1016/j.jhydrol.2021.126857
14. 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
15. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi 10.3390/resources11120111
16. 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
17. 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
18. IoT-Gewässergüte-Monitoring mittels KI-basierter Hauptionenzerlegung aus Leitfähigkeitsdaten im Erfteinzugsgebiet M. Delker et al. 10.1007/s35147-023-1874-7
19. 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
20. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
21. 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
22. Unraveling the Effects of Long-Distance Water Transfer for Ecological Recharge W. Ding et al. 10.1061/(ASCE)WR.1943-5452.0001272
23. 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
24. 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
25. Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model X. Yang et al. 10.3390/w14132018
26. 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
27. Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction A. Meyal et al. 10.3389/frwa.2020.574917
28. Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
29. 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
30. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
31. 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
32. Developing data-driven learning models to predict urban stormwater runoff volume R. Wood-Ponce et al. 10.1080/1573062X.2024.2312514
33. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. 10.1016/j.jhydrol.2024.130861
34. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
35. 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
36. 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
37. Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network C. Chen et al. 10.3390/s21041238
38. 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
39. 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
40. 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
41. 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
42. 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
43. Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate J. Huang et al. 10.1007/s11227-022-04827-3
44. 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
45. Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2 T. Dantzer & B. Kerkez 10.1016/j.envsoft.2024.106137
46. Comparison of data-driven techniques for daily streamflow forecasting P. de Bourgoing & A. Malekian 10.1007/s13762-023-05131-0
47. An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points Y. Liu et al. 10.3390/app122312334
48. Analysis of reservoir outflow using deep learning model S. Choudhary & S. Ghosh 10.1007/s40808-023-01803-5
49. Evolution and attribution of ecological flow in the Xiangjiang River basin since 1961 W. Guo et al. 10.1007/s11356-023-29626-y
50. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. 10.1016/j.envsoft.2020.104926
51. Dynamic prediction of mechanized shield tunneling performance R. Wang et al. 10.1016/j.autcon.2021.103958
52. Prediction of energy consumption in campus buildings using long short-term memory M. Faiq et al. 10.1016/j.aej.2022.12.015
53. 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
54. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. 10.1038/s43017-023-00452-7
55. A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River H. Xue et al. 10.3390/su15107819
56. Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. 10.5194/essd-13-2001-2021
57. 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
58. Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory R. Yuan et al. 10.3389/feart.2021.621780
59. Modern Strategies for Time Series Regression S. Clark et al. 10.1111/insr.12432
60. Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms T. Kesavavarthini et al. 10.1002/joc.8056
61. Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition J. Shen et al. 10.3390/w14142241
62. 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
63. Multi-station runoff-sediment modeling using seasonal LSTM models V. Nourani & N. Behfar 10.1016/j.jhydrol.2021.126672
64. A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation M. Mirzaei et al. 10.3390/su132313384
65. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin S. Nimai et al. 10.3390/w15213758
66. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
67. Improving streamflow prediction in the WRF-Hydro model with LSTM networks K. Cho & Y. Kim 10.1016/j.jhydrol.2021.127297
68. Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
69. Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm M. Zakhrouf et al. 10.1080/02723646.2021.1943126
70. 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
71. Bias learning improves data driven models for streamflow prediction Y. Lin et al. 10.1016/j.ejrh.2023.101557
72. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
73. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
74. 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
75. 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
76. 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
77. A Nonlinear Dynamical Model for Monthly Runoff Forecasting in Situations of Small Samples N. Liu et al. 10.1007/s11004-023-10099-1
78. 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
79. 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
80. 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
81. Application of temporal convolutional network for flood forecasting Y. Xu et al. 10.2166/nh.2021.021
82. 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
83. A Hierarchical Temporal Scale Framework for Data‐Driven Reservoir Release Modeling Q. Longyang & R. Zeng 10.1029/2022WR033922
84. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
85. Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget N. Martin 10.3390/hydrology10060129
86. 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
87. 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
88. Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework N. Fernandez et al. 10.1016/j.ejrh.2022.101243
89. 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
90. Data-driven modeling of municipal water system responses to hydroclimate extremes R. Johnson et al. 10.2166/hydro.2023.170
91. Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network P. Kashyap et al. 10.1109/JSEN.2021.3069266
92. 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
93. 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
94. 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
95. Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture S. Topp et al. 10.1029/2022WR033880
96. 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
97. 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
98. 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
99. 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
100. 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
101. Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise A. Trucco et al. 10.1109/JOE.2022.3223406
102. 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
103. 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
104. Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction D. Kim et al. 10.3390/app12136699
105. 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
106. Inflow Prediction of Youjiang Reservoir Based on EMD-LSTM model 志. 潘 10.12677/JWRR.2022.111002
107. 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
108. Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting X. Le et al. 10.3390/w11071387
109. Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS M. Shen & T. Chui 10.1016/j.jhydrol.2023.130255
110. 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
111. Convolutional neural network and long short-term memory models for ice-jam predictions F. Madaeni et al. 10.5194/tc-16-1447-2022
112. A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
113. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
114. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
115. Real-time streamflow forecasting: AI vs. Hydrologic insights W. Krajewski et al. 10.1016/j.hydroa.2021.100110
116. 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
117. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
118. 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
119. Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning P. Ai et al. 10.1109/ACCESS.2023.3241995
120. 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
121. Study on Forecasting Break-Up Date of River Ice in Heilongjiang Province Based on LSTM and CEEMDAN M. Liu et al. 10.3390/w15030496
122. A hydrological process-based neural network model for hourly runoff forecasting S. Gao et al. 10.1016/j.envsoft.2024.106029
123. Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction Y. Wu et al. 10.3390/su14116612
124. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
125. 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
126. 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
127. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al. 10.3389/frwa.2022.886964
128. 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
129. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al. 10.5194/acp-24-7041-2024
130. Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning M. Ren et al. 10.1007/s11269-024-03911-y
131. Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model D. Costa Silva et al. 10.3390/en14227707
132. Deep Learning-Based Univariate Prediction of Daily Rainfall: Application to a Flood-Prone, Data-Deficient Country I. Necesito et al. 10.3390/atmos14040632
133. 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
134. 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
135. 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
136. 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
137. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
138. 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
139. Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework R. Mo et al. 10.1016/j.jhydrol.2023.129736
140. Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
141. 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
142. 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
143. Auf dem Weg zu besseren Wasserstand-Durchfluss-Beziehungen H. Oppel et al. 10.1007/s35147-023-1879-2
144. 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
145. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
146. 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
147. Machine-learning methods for stream water temperature prediction M. Feigl et al. 10.5194/hess-25-2951-2021
148. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
149. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D. Kim et al. 10.3390/w14030466
150. 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
151. 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
152. A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting Z. Cui et al. 10.2166/nh.2021.016
153. An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting I. Huang et al. 10.1007/s00477-021-02085-y
154. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al. 10.1038/s41598-023-35963-2
155. 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
156. 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
157. Hybrid CNN-LSTM models for river flow prediction X. Li et al. 10.2166/ws.2022.170
158. Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction Y. Yan et al. 10.2166/hydro.2023.006
159. Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing I. Ortiz-Garces et al. 10.3390/app14051737
160. 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
161. 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
162. Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation Y. Xu et al. 10.1016/j.jhydrol.2022.127553
163. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
164. Advancing IoT-Based Smart Irrigation R. Togneri et al. 10.1109/IOTM.0001.1900046
165. 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
166. Forecasting estuarine salt intrusion in the Rhine–Meuse delta using an LSTM model B. Wullems et al. 10.5194/hess-27-3823-2023
167. 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
168. 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
169. 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
170. 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
171. Evaluating the Marginal Utility of Two-Stage Hydropower Scheduling W. Ding et al. 10.1061/(ASCE)WR.1943-5452.0001556
172. A coupled model applied to complex river–lake systems Q. Zhang et al. 10.1080/02626667.2023.2285441
173. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
174. Quantifying the Impact of Cascade Reservoirs on Streamflow, Drought, and Flood in the Jinsha River Basin K. Zhang et al. 10.3390/su15064989
175. 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
176. 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
177. Streamflow and rainfall forecasting by two long short-term memory-based models L. Ni et al. 10.1016/j.jhydrol.2019.124296
178. 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
179. 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
180. 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
181. 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
182. 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
183. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
184. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. 10.2166/wst.2024.110
185. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
186. 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
187. 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
188. 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
189. Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes F. Simanjuntak et al. 10.3390/rs14235950
190. 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
191. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
192. 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
193. A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism Z. Dai et al. 10.3390/w15040670
194. 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
195. 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
196. Application of deep learning in automatic detection of technical and tactical indicators of table tennis F. Qiao & Z. Lv 10.1371/journal.pone.0245259
197. 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
198. Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application F. Zeng et al. 10.3390/w12082201
199. Predicting Playa Inundation Using a Long Short‐Term Memory Neural Network K. Solvik et al. 10.1029/2020WR029009
200. 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
201. 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
202. 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
203. 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
204. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu 10.1109/ACCESS.2024.3384066
205. EMULATION OF URBAN RUNOFF MODEL BY DEEP LEARNING FOR BENCHMARK VIRTUAL HYETO AND HYDROGRAPH S. FUJIZUKA et al. 10.2208/jscejer.75.I_289
206. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
207. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
208. 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
209. Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia M. Adli Zakaria et al. 10.1016/j.heliyon.2023.e17689
210. 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
211. Exploring the best sequence LSTM modeling architecture for flood prediction W. Li et al. 10.1007/s00521-020-05334-3
212. A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation S. Wang et al. 10.2166/nh.2024.035
213. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
214. 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
215. 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
216. 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
217. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502
218. 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
219. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction J. Choi et al. 10.3390/w14182910
220. LSTM with spatiotemporal attention for IoT-based wireless sensor collected hydrological time-series forecasting J. Huang et al. 10.1007/s13042-023-01836-3
221. Persistent neural calibration for discharges modelling in drought-stressed catchments I. Pulido-Calvo et al. 10.1016/j.eswa.2024.123785
222. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
223. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965
224. 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
225. 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
226. Artificial intelligence applications in the field of streamflow: a bibliometric analysis of recent trends G. Özdoğan Sarıkoç 10.1080/02626667.2024.2356006
227. 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
228. 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
229. Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling G. Gelete 10.1007/s12145-023-01041-4
230. 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
231. Long lead-time daily and monthly streamflow forecasting using machine learning methods M. Cheng et al. 10.1016/j.jhydrol.2020.125376
232. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
233. 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
234. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
235. Runoff and sediment simulation of terraces and check dams based on underlying surface conditions G. Li et al. 10.1007/s13201-022-01828-8
236. 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
237. An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System I. Hayder et al. 10.3390/pr11020481
238. Prediction models for urban flood evolution for satellite remote sensing R. Lammers et al. 10.1016/j.jhydrol.2021.127175
239. 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
240. Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach P. Li et al. 10.3390/w14060993
241. 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
242. 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
243. 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
244. Transformer Based Water Level Prediction in Poyang Lake, China J. Xu et al. 10.3390/w15030576
245. 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
246. 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
247. 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
248. 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
249. 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
250. 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
251. 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
252. 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
253. 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
254. 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
255. 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
256. Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments L. Zhong et al. 10.1029/2022WR034118
257. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. 10.1029/2021WR030185
258. 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
259. CREATION AND VERIFICATION OF A PRETRAINED MODEL FOR RIVER FLOOD PREDICTIONS N. KIMURA et al. 10.2208/jscejj.23-16147
260. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
261. Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing Y. Yang & T. Chui 10.1029/2020WR029471
262. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
263. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
264. Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment J. Wu et al. 10.3390/w11071327
265. Deep learning, hydrological processes and the uniqueness of place K. Beven 10.1002/hyp.13805
266. 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
267. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al. 10.1016/j.jhydrol.2022.128577
268. 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
269. Surface and high-altitude combined rainfall forecasting using convolutional neural network P. Zhang et al. 10.1007/s12083-020-00938-x
270. 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
271. 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
272. A review of machine learning applications to coastal sediment transport and morphodynamics E. Goldstein et al. 10.1016/j.earscirev.2019.04.022
273. 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
274. 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
275. Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach S. Chen et al. 10.1016/j.jhydrol.2023.129734
276. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
277. 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
278. 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
279. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
280. Water quality prediction based on sparse dataset using enhanced machine learning S. Huang et al. 10.1016/j.ese.2024.100402
281. 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
282. 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
283. Which riverine water quality parameters can be predicted by meteorologically-driven deep learning? S. Huang et al. 10.1016/j.scitotenv.2024.174357
284. 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
285. 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
286. A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting S. Khorram & N. Jehbez 10.1007/s11269-023-03541-w
287. 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
288. A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration J. Chen et al. 10.5194/hess-25-6041-2021
289. 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
290. Applications and interpretations of different machine learning models in runoff and sediment discharge simulations J. Miao et al. 10.1016/j.catena.2024.107848
291. 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
292. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al. 10.1016/j.jhydrol.2023.129474
293. 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
294. Physics-Informed Data-Driven Model for Predicting Streamflow: A Case Study of the Voshmgir Basin, Iran P. Parisouj et al. 10.3390/app12157464
295. 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
296. Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases M. Shoaib et al. 10.1007/s42979-021-00764-9
297. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
298. State Parameter Based Liquefaction Probability Evaluation K. Kumar et al. 10.1007/s40891-023-00495-2
299. Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management H. Afzaal et al. 10.3390/app10051621
300. Enhanced Long Short-Term Memory Model for Runoff Prediction R. Feng et al. 10.1061/(ASCE)HE.1943-5584.0002035
301. 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
302. Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area Y. Su et al. 10.3390/atmos14091392
303. 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network Y. Kim et al. 10.1109/ACCESS.2022.3179001
304. 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
305. User-focused evaluation of National Ecological Observatory Network streamflow estimates S. Rhea et al. 10.1038/s41597-023-01983-w
306. Probabilistic runoff forecasting considering stepwise decomposition framework and external factor integration structure C. Cao et al. 10.1016/j.eswa.2023.121350
307. 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
308. Application of deep learning to large scale riverine flow velocity estimation M. Forghani et al. 10.1007/s00477-021-01988-0
309. 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
310. Spatial-temporal behavior of precipitation driven karst spring discharge in a mountain terrain X. Song et al. 10.1016/j.jhydrol.2022.128116
311. Developing a deep learning model for the simulation of micro-pollutants in a watershed D. Yun et al. 10.1016/j.jclepro.2021.126858
312. 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
313. Joint Spatial and Temporal Modeling for Hydrological Prediction Q. Zhao et al. 10.1109/ACCESS.2020.2990181
314. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
315. Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas F. Forghanparast & G. Mohammadi 10.3390/w14192972
316. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
317. Snowmelt-Driven Streamflow Prediction Using Machine Learning Techniques (LSTM, NARX, GPR, and SVR) S. Thapa et al. 10.3390/w12061734
318. Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China W. Wang et al. 10.3390/w16111589
319. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels E. Chen et al. 10.1016/j.envsoft.2024.106072
320. 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
321. 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
322. 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
323. A Novel Instrument for Bed Dynamics Observation Supports Machine Learning Applications in Mangrove Biogeomorphic Processes Z. Hu et al. 10.1029/2020WR027257
324. 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
325. 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
326. 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
327. 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
328. 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
329. Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm Y. Jhong et al. 10.1007/s11269-023-03713-8
330. 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
331. Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry M. Forghani et al. 10.1016/j.advwatres.2022.104323
332. Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation X. Zhang et al. 10.1162/dint_a_00221
333. 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
334. 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
335. Simulation of spring discharge using graph neural networks at Niangziguan Springs, China Y. Gai et al. 10.1016/j.jhydrol.2023.130079
336. 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
337. Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms Y. Essam et al. 10.1038/s41598-022-07693-4
338. Rainfall forecasting in upper Indus basin using various artificial intelligence techniques M. Hammad et al. 10.1007/s00477-021-02013-0
339. 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
340. Deep insight into daily runoff forecasting based on a CNN-LSTM model H. Deng et al. 10.1007/s11069-022-05363-2
341. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
342. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al. 10.1002/joc.7738
343. WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting I. Demir et al. 10.5194/essd-14-5605-2022
344. 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
345. 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
346. Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia S. Clark 10.1016/j.envsoft.2022.105295
347. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
348. Short-term runoff prediction using deep learning multi-dimensional ensemble method G. Liu et al. 10.1016/j.jhydrol.2022.127762
349. 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
350. 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
351. 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
352. Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons M. Boucher et al. 10.1029/2019WR026226
353. 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
354. 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
355. 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
356. 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
357. Water level prediction of Liuxihe Reservoir based on improved long short-term memory neural network Y. Li et al. 10.2166/ws.2023.282
358. Deep dependence in hydroclimatological variables T. Lee & J. Kim 10.1007/s10489-024-05345-w
359. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al. 10.1175/JHM-D-20-0072.1
360. 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
361. 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
362. 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
363. 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
364. 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
365. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
366. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
367. Predicting mean annual and mean monthly streamflow in Colorado ungauged basins A. Eurich et al. 10.1002/rra.3778
368. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
369. 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
370. A 500-year annual runoff reconstruction for 14 selected European catchments S. Nasreen et al. 10.5194/essd-14-4035-2022
371. 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
372. 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
373. 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
374. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
375. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
376. 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
377. 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
378. 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
379. 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
380. 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
381. Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level J. Kim et al. 10.3390/w14091512
382. Comparison of machine learning techniques for reservoir outflow forecasting O. García-Feal et al. 10.5194/nhess-22-3859-2022
383. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
384. Short-term Lake Erie algal bloom prediction by classification and regression models H. Ai et al. 10.1016/j.watres.2023.119710
385. Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques Y. Chang et al. 10.3390/w15234100
386. Surrogate optimization of deep neural networks for groundwater predictions J. Müller et al. 10.1007/s10898-020-00912-0
387. 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
388. 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
389. Performance of statistical and machine learning ensembles for daily temperature downscaling X. Li et al. 10.1007/s00704-020-03098-3
390. 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
391. 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
392. Multilayer self‐attention residual network for code search H. Hu et al. 10.1002/cpe.7650
393. 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
394. 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
395. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
396. 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
397. 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
398. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
399. Deep Lagged-Wavelet for monthly rainfall forecasting in a tropical region E. Vivas et al. 10.1007/s00477-022-02323-x
400. Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India B. Supreetha et al. 10.1155/2020/8685724
401. 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
402. 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
403. 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
404. 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
405. 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
406. A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. 10.2166/nh.2024.016
407. Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths Y. Zhou et al. 10.1029/2022WR033214
408. In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM T. Zhang et al. 10.3390/rs15102693
409. 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
410. 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
411. 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
412. 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
413. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
414. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
415. 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
416. 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
417. Flash Flood Forecasting Based on Long Short-Term Memory Networks T. Song et al. 10.3390/w12010109
418. 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
419. An Approach Based on Recurrent Neural Networks and Interactive Visualization to Improve Explainability in AI Systems W. Villegas-Ch et al. 10.3390/bdcc7030136
420. Machine learning-based techniques for land subsidence simulation in an urban area J. Liu et al. 10.1016/j.jenvman.2024.120078
421. 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
422. Large-scale flash flood warning in China using deep learning G. Zhao et al. 10.1016/j.jhydrol.2021.127222
423. 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
424. 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
425. 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
426. Neural Structures to Predict River Stages in Heavily Urbanized Catchments A. Chiacchiera et al. 10.3390/w14152330
427. Comparison of Deep Learning Techniques for River Streamflow Forecasting X. Le et al. 10.1109/ACCESS.2021.3077703
428. 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
429. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
430. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
431. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al. 10.1007/s00521-022-07523-8
432. 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
433. 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
434. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
435. 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
436. 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
437. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
438. 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
439. 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
440. A high-efficiency spaceborne processor for hybrid neural networks S. Wang et al. 10.1016/j.neucom.2023.126230
441. 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
442. Improved runoff forecasting performance through error predictions using a deep-learning approach H. Han & R. Morrison 10.1016/j.jhydrol.2022.127653
443. Machine learning for predicting discharge fluctuation of a karst spring in North China S. Cheng et al. 10.1007/s11600-020-00522-0
444. 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
445. 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
446. 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
447. 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
448. 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
449. 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
450. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
451. Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model Q. Cao et al. 10.1111/jfr3.12827
452. 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
453. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
454. Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds Y. Zhu et al. 10.1109/ACCESS.2020.2971264
455. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso 10.18034/ajase.v8i1.10
456. 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
457. 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
458. 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
459. 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
460. 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
461. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
462. 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
463. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
464. Runoff forecasting model based on variational mode decomposition and artificial neural networks X. Jing et al. 10.3934/mbe.2022076
465. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
466. AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM C. Yang et al. 10.1109/ACCESS.2022.3154812
467. 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
468. Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes E. Orland et al. 10.1029/2020GL088731
469. A review of Earth Artificial Intelligence Z. Sun et al. 10.1016/j.cageo.2022.105034
470. 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
471. 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
472. Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al. 10.3390/ijgi12110464
473. 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
474. On the use of convolutional deep learning to predict shoreline change E. Gomez-de la Peña et al. 10.5194/esurf-11-1145-2023
475. 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
476. 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
477. Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al. 10.1007/s11356-024-33594-2
478. Impact of training data size on the LSTM performances for rainfall–runoff modeling T. Boulmaiz et al. 10.1007/s40808-020-00830-w
479. 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
480. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
481. 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
482. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
483. 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
484. Research on Precipitation Forecast Based on LSTM–CP Combined Model Y. Guo et al. 10.3390/su132111596
485. 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
486. 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
487. 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
488. 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
489. Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
490. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al. 10.1007/s11356-021-17084-3
491. 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
492. A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils J. Zhou et al. 10.3390/s23125599
493. Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision T. Hascoet et al. 10.3390/rs16010170
494. 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
495. 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
496. 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
497. Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing S. Wang et al. 10.1051/jnwpu/20213930510
498. 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
499. 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
500. Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
501. 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
502. 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
503. Forecast of rainfall distribution based on fixed sliding window long short-term memory C. Chen et al. 10.1080/19942060.2021.2009374
504. 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
505. 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
506. 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
507. Machine learning-based surrogate model assisting stochastic model predictive control of urban drainage systems X. Luo et al. 10.1016/j.jenvman.2023.118974
508. 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
509. 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
510. CDLSTM: A Novel Model for Climate Change Forecasting M. Anul Haq 10.32604/cmc.2022.023059
511. Analysis of rainfall and temperature using deep learning model S. Choudhary & S. Ghosh 10.1007/s00704-023-04493-2
512. 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
513. 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
514. 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
515. Hierarchical prediction of soil water content time series F. Leij et al. 10.1016/j.catena.2021.105841
516. 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
517. 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
518. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
519. 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
520. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
521. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
522. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
523. SUPPORT SYSTEM FOR SPECULATION BY EXCHANGE TRADES FUNDS G. Tumaševičius & N. Maknickienė 10.3846/mla.2022.15870
524. 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
525. 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
526. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al. 10.1016/j.envsoft.2021.105112
527. 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
528. Solving flood problems with deep learning technology: Research status, strategies, and future directions H. Li et al. 10.1002/sd.3074
529. 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
530. Flood Prediction and Uncertainty Estimation Using Deep Learning V. Gude et al. 10.3390/w12030884
531. 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
532. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks A. Abbas et al. 10.1016/j.jhydrol.2020.125370
533. 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
534. 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
535. 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
536. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model R. Barzegar et al. 10.1007/s00477-020-01776-2
537. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
538. 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
539. High temporal resolution urban flood prediction using attention-based LSTM models L. Zhang et al. 10.1016/j.jhydrol.2023.129499
540. Predicting stock market index using LSTM H. Bhandari et al. 10.1016/j.mlwa.2022.100320
541. Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation H. Han et al. 10.3390/w13040437
542. 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
543. 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
544. Deep learning convolutional neural network in rainfall–runoff modelling S. Van et al. 10.2166/hydro.2020.095
545. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
546. 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
547. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
548. 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
549. 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
550. 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
551. 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
552. 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
553. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
554. 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
555. 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
556. 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
557. 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
558. 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
559. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
560. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
561. 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
562. 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
563. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. 10.1016/j.xinn.2024.100617
564. A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework Z. Xu et al. 10.3390/w13233390
565. Long-term prediction of runoff of Heiher River in China based on EMD-LSTM networks H. Xue et al. 10.1051/matecconf/202439501005
566. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
567. Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model L. Ni et al. 10.1016/j.jhydrol.2020.124901
568. Daily runoff forecasting based on data-augmented neural network model X. Bi et al. 10.2166/hydro.2020.017
569. 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
570. 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
571. Crop yield prediction based on reanalysis and crop phenology data in the agroclimatic zones S. Yeşilköy & I. Demir 10.1007/s00704-024-05046-x
572. Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data C. Yang et al. 10.3390/math10162936
573. Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
574. Optimizing seasonal discharge predictions: a hybridized approach with AI and non-linear models S. Sharma & M. Patel 10.1007/s41939-024-00401-x
575. Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
576. 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
577. 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
578. Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry S. Hubbard et al. 10.1002/hyp.13807
579. 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
580. 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
581. 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
582. 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
583. Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation H. Gui et al. 10.1002/clen.202000381
584. 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
585. Urban flood forecasting using a hybrid modeling approach based on a deep learning technique H. Moon et al. 10.2166/hydro.2023.203
586. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
587. 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
588. 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
589. 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
590. A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones Y. Wang et al. 10.1029/2023WR035194
591. Forecasting of monthly precipitation based on ensemble empirical mode decomposition and Bayesian model averaging S. Luo et al. 10.3389/feart.2022.926067
592. 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
593. Deep Convolutional LSTM for improved flash flood prediction P. Oddo et al. 10.3389/frwa.2024.1346104
594. 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
595. 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
596. Comparative applications of data-driven models representing water table fluctuations J. Jeong & E. Park 10.1016/j.jhydrol.2019.02.051
597. 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
598. 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
599. Using SARIMA–CNN–LSTM approach to forecast daily tourism demand K. He et al. 10.1016/j.jhtm.2021.08.022
600. Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction H. Choi et al. 10.3390/w14121878
601. Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management L. Zhang et al. 10.1016/j.envres.2024.118267
602. 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
603. 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
604. 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
605. 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
606. 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
607. Extreme pressure coefficients: modelling a hydraulic jump using deep-learning based methods S. Mousavi et al. 10.1007/s12046-024-02515-x
608. Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation V. Sessa et al. 10.3390/cleantechnol3040050
609. 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
610. Exploring deep learning capabilities for surge predictions in coastal areas T. Tiggeloven et al. 10.1038/s41598-021-96674-0
611. 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
612. An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach X. Wang et al. 10.1080/02626667.2021.1937631
613. Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
614. River Flooding Forecasting and Anomaly Detection Based on Deep Learning S. Miau & W. Hung 10.1109/ACCESS.2020.3034875
615. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
616. Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction Z. Li et al. 10.3390/w13040575
617. A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting R. Zhou et al. 10.1016/j.jhydrol.2024.131128
618. 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
619. An efficient LSTM network for predicting the tailing and multi-peaked breakthrough curves J. Niu et al. 10.1016/j.jhydrol.2023.129914
620. Prediction of Cloud Fractional Cover Using Machine Learning H. Svennevik et al. 10.3390/bdcc5040062
621. Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network R. Kilsdonk et al. 10.3390/hydrology9060105
622. Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal S. Thapa et al. 10.1007/s10661-021-09197-6
623. 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
624. Simulation of Pollution Load at Basin Scale Based on LSTM-BP Spatiotemporal Combination Model L. Li et al. 10.3390/w13040516
625. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
626. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
627. Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs R. Jougla & R. Leconte 10.3390/w14040552
628. Research on Runoff Simulations Using Deep-Learning Methods Y. Liu et al. 10.3390/su13031336
629. An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks L. Windheuser et al. 10.1029/2022EA002385
630. 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
631. Neural Network Emulation of the Formation of Organic Aerosols Based on the Explicit GECKO‐A Chemistry Model J. Schreck et al. 10.1029/2021MS002974
632. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
633. 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
634. Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models G. Li et al. 10.1016/j.jclepro.2024.141228
635. A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
636. Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
637. Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model S. Guniganti et al. 10.1080/02626667.2024.2335294
638. Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model S. Huang et al. 10.3390/w15091704
639. 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
640. 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
641. 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
642. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
643. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
644. Deep Learning for Time Series Forecasting: Advances and Open Problems A. Casolaro et al. 10.3390/info14110598
645. A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop K. Dolaptsis et al. 10.3390/agriculture14020210
646. 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
647. Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning H. Kang et al. 10.1007/s12555-019-0984-6
648. Using long short-term memory networks for river flow prediction W. Xu et al. 10.2166/nh.2020.026
649. 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
650. Short- and mid-term forecasts of actual evapotranspiration with deep learning E. Babaeian et al. 10.1016/j.jhydrol.2022.128078
651. Evaluation of Snowmelt Estimation Techniques for Enhanced Spring Peak Flow Prediction J. Agnihotri & P. Coulibaly 10.3390/w12051290
652. 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
653. 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
654. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
655. 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
656. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
657. 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
658. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
659. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
660. 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
661. 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
662. Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm T. Kusudo et al. 10.3390/w14010055
663. A simplified modeling approach for optimization of urban river systems W. Feng et al. 10.1016/j.jhydrol.2023.129689
664. 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
665. Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models X. Li et al. 10.3390/su141811149
666. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
667. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
668. Estimating river discharge from rainfall satellite data through simple statistical models P. Birocchi et al. 10.1007/s00704-023-04459-4
669. A hybrid deep learning algorithm and its application to streamflow prediction Y. Lin et al. 10.1016/j.jhydrol.2021.126636
670. 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
671. Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. 10.1016/j.ejrh.2023.101471
672. Forecasting the River Water Discharge by Artificial Intelligence Methods A. Bărbulescu & L. Zhen 10.3390/w16091248
673. Simulation of Rainfall-Runoff process using SWAT model in Bouhamdane watershed, Algeria B. Abdelkebir et al. 10.2298/GSGD2302279A
674. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
675. 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
676. 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
677. Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model Y. Zhang et al. 10.1016/j.jclepro.2022.131724
678. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability C. Song 10.1016/j.jhydrol.2021.127324
679. 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
680. On the role of the architecture for spring discharge prediction with deep learning approaches R. Zhou & Y. Zhang 10.1002/hyp.14737
681. The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China M. Liu et al. 10.3390/w12020440
682. 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
683. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
684. 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
685. 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
686. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
687. Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting H. Apaydin et al. 10.3390/w12051500
688. A machine learning-based approach for generating high-resolution soil moisture from SMAP products Y. Zhang et al. 10.1080/10106049.2022.2105406
689. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
690. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
691. 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
692. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
693. Lake Level Prediction using Feed Forward and Recurrent Neural Networks B. Hrnjica & O. Bonacci 10.1007/s11269-019-02255-2
694. 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
695. 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
696. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning F. Azad et al. 10.1145/3672556
697. Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River Y. Liu et al. 10.1007/s12205-023-2469-7
698. 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
699. Deep learning-based streamflow prediction for western Himalayan river basins T. Majeed et al. 10.1007/s13198-024-02403-x
700. 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
701. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
702. Modelling the effect of cascade reservoir regulation on ice-jam flooding M. Liu et al. 10.1016/j.jhydrol.2024.131358
703. Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction Y. Xu et al. 10.1007/s11269-022-03346-3
704. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
705. Reconstruction of GRACE Total Water Storage Through Automated Machine Learning A. Sun et al. 10.1029/2020WR028666
706. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
707. 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
708. ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data P. Hosseinzadeh et al. 10.3390/hydrology10020029
709. 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
710. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
711. 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
712. 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
713. 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
714. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
715. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C. Shen et al. 10.5194/hess-22-5639-2018
716. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
717. Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models A. Bennett & B. Nijssen 10.1029/2020WR029328
718. Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation C. Hu et al. 10.3390/w10111543
719. Applications of Deep Learning Models for Forecasting and Modelling Rainwater in Moscow A. Ramadhan et al. 10.1051/bioconf/20249700126
720. A deep learning model to predict lower temperatures in agriculture M. Guillén-Navarro et al. 10.3233/AIS-200546
721. 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
722. Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting Y. Tian et al. 10.3390/w10111655
723. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen 10.1029/2018WR022643
724. 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
725. A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning Z. Xiang et al. 10.1029/2019WR025326