343 citations as recorded by crossref.

1. Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China Q. Zhu et al. 10.5194/hess-28-1665-2024
2. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
3. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
4. Bias learning improves data driven models for streamflow prediction Y. Lin et al. 10.1016/j.ejrh.2023.101557
5. Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers A. Izadi et al. 10.1016/j.eswa.2024.124139
6. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
7. 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
8. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
9. 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
10. Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020) J. Willard et al. 10.1002/lol2.10249
11. Machine-learning methods for stream water temperature prediction M. Feigl et al. 10.5194/hess-25-2951-2021
12. 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
13. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
14. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
15. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
16. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al. 10.5194/hess-27-2357-2023
17. Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere V. Tran et al. 10.1038/s41597-024-03483-x
18. 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
19. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
20. Technical note: Investigating the potential for smartphone-based monitoring of evapotranspiration and land surface energy-balance partitioning A. Teuling et al. 10.5194/hess-28-3799-2024
21. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
22. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
23. Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea H. Yang et al. 10.3390/f14061131
24. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
25. A Multilayer Perceptron Model for Stochastic Synthesis E. Rozos et al. 10.3390/hydrology8020067
26. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al. 10.1016/j.ejrh.2021.100930
27. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
28. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
29. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
30. Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation J. Zhang et al. 10.1016/j.jhydrol.2024.132549
31. Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning P. Le et al. 10.1016/j.jhydrol.2024.131562
32. 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
33. 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
34. Soil Moisture to Runoff (SM2R): A Data‐Driven Model for Runoff Estimation Across Poorly Gauged Asian Water Towers Based on Soil Moisture Dynamics X. Li et al. 10.1029/2022WR033597
35. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta 10.3390/su141610447
36. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
37. 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
38. 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
39. Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management S. Zipper et al. 10.1111/1752-1688.12998
40. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
41. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
42. 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
43. Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model L. Zhang et al. 10.1007/s11269-022-03224-y
44. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
45. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
46. Global big data laboratory experiment, integrated with kernel-based algorithm with an improved nonlinear ensemble for compressive strength modeling B. Salami et al. 10.1038/s41598-024-58908-9
47. Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms A. Ivanov et al. 10.24057/2071-9388-2022-087
48. Advancing the science of headwater streamflow for global water protection H. Golden et al. 10.1038/s44221-024-00351-1
49. 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
50. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
51. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
52. Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al. 10.1016/j.envsoft.2024.106053
53. Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA G. Goldrich‐Middaugh et al. 10.1029/2022WR032155
54. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
55. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
56. Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al. 10.1007/s11269-024-03780-5
57. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
58. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
59. 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
60. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
61. 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
62. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
63. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
64. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
65. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
66. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
67. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
68. 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
69. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al. 10.1007/s11356-021-17084-3
70. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
71. Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. 10.5194/hess-28-5443-2024
72. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
73. Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool N. Majeske et al. 10.1016/j.envsoft.2022.105400
74. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
75. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
76. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
77. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
78. Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel 10.1007/s44290-024-00084-w
79. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
80. 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
81. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
82. Data-driven effects of human activities and environmental factors on inland aquatic dissolved organic matter in China: Insights from machine learning Y. Ma et al. 10.1016/j.jclepro.2024.144034
83. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
84. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965
85. Towards a comprehensive uncertainty assessment in environmental research and decision support P. Reichert 10.2166/wst.2020.032
86. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
87. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al. 10.1175/JHM-D-20-0072.1
88. 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
89. Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin M. Merizalde et al. 10.3389/frwa.2023.1233899
90. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
91. Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms Y. Sun et al. 10.1002/ird.2942
92. 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
93. 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
94. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
95. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
96. Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season K. Fang et al. 10.3389/frwa.2024.1456647
97. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
98. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region K. Ekpetere et al. 10.3390/hydrology10040078
99. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
100. 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
101. 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
102. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
103. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. 10.1016/j.jhydrol.2021.126455
104. Global soil moisture data derived through machine learning trained with in-situ measurements S. O. & R. Orth 10.1038/s41597-021-00964-1
105. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
106. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
107. 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
108. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
109. Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies A. DelSanto et al. 10.3390/w15152813
110. 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
111. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
112. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
113. 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
114. Leveraging synthetic assimilation of remote sensing with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins S. Fang et al. 10.1088/1748-9326/ad8808
115. Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al. 10.5194/hess-26-4553-2022
116. Classification-enhanced LSTM model for predicting river water levels J. Luo et al. 10.1016/j.jhydrol.2024.132535
117. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
118. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
119. 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
120. 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
121. High-frequency data significantly enhances the prediction ability of point and interval estimation X. Liu et al. 10.1016/j.scitotenv.2023.169289
122. 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
123. An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al. 10.5194/hess-27-2035-2023
124. Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al. 10.1002/hyp.15270
125. In-stream <i>Escherichia coli</i> modeling using high-temporal-resolution data with deep learning and process-based models A. Abbas et al. 10.5194/hess-25-6185-2021
126. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
127. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al. 10.1016/j.envsoft.2021.105112
128. Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al. 10.1016/j.jenvman.2023.118744
129. A parsimonious setup for streamflow forecasting using CNN-LSTM S. Pokharel & T. Roy 10.2166/hydro.2024.114
130. 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
131. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al. 10.1038/s41598-023-35963-2
132. Mitigating flood risk at Shah Alam, Malaysia for sustainable development A. Leeonis et al. 10.1007/s43621-024-00504-y
133. Regionalisierung hydrologischer Modelle mit Function Space Optimization M. Feigl et al. 10.1007/s00506-021-00766-0
134. 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
135. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
136. 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
137. A coupled model applied to complex river–lake systems Q. Zhang et al. 10.1080/02626667.2023.2285441
138. 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
139. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
140. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
141. Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data L. Qian et al. 10.1016/j.jhydrol.2024.131755
142. A novel operational water quality mobile prediction system with LSTM-Seq2Seq model L. Xie et al. 10.1016/j.envsoft.2024.106290
143. 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
144. A hydrological knowledge-informed LSTM model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the Tibetan plateau S. Hou et al. 10.1016/j.jhydrol.2024.132409
145. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
146. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
147. Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics S. Fischer et al. 10.1016/j.jhydrol.2024.132361
148. Deep learning model on rates of change for multi-step ahead streamflow forecasting W. Tan et al. 10.2166/hydro.2023.001
149. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems A. Rupe et al. 10.1088/1367-2630/ac95b7
150. Issues in generating stochastic observables for hydrological models K. Beven 10.1002/hyp.14203
151. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
152. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
153. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
154. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
155. Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins S. Zhu et al. 10.1016/j.jhydrol.2022.128727
156. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
157. Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation N. Martin & J. White 10.3390/w16192758
158. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
159. 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
160. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
161. Long-term Reservoir Inflow Forecasts: Enhanced Water Supply and Inflow Volume Accuracy Using Deep Learning Z. Herbert et al. 10.1016/j.jhydrol.2021.126676
162. 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
163. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks A. Abbas et al. 10.1016/j.jhydrol.2020.125370
164. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
165. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
166. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
167. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
168. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
169. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
170. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al. 10.3389/frwa.2022.886964
171. 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
172. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al. 10.5194/acp-24-7041-2024
173. 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
174. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D. Feng et al. 10.1029/2022WR032404
175. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
176. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
177. Forecasting Floods Using Deep Learning Models: A Longitudinal Case Study of Chenab River, Pakistan K. Aatif et al. 10.1109/ACCESS.2024.3445586
178. Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation S. He et al. 10.3390/su13073645
179. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al. 10.1016/j.ejrh.2024.102062
180. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
181. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
182. Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction P. Huang 10.1111/jfr3.13050
183. A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models K. Beven 10.1080/02626667.2024.2332616
184. Classification and Zoning of Rivers by Their Water Regime: History, Methodology, and Perspectives N. Frolova et al. 10.1134/S0097807821020056
185. Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al. 10.3390/ijgi12110464
186. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
187. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
188. Enhancing Hydrological Variable Prediction through Multitask LSTM Models Y. Yan et al. 10.3390/w16152156
189. 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
190. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh 10.1029/2022WR034188
191. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
192. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
193. Artificial intelligence can provide accurate forecasts of extreme floods at global scale 10.1038/d41586-024-00835-w
194. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
195. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin S. Nimai et al. 10.3390/w15213758
196. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
197. 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
198. Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters A. Anshuman & T. Eldho 10.1016/j.jhydrol.2022.127662
199. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al. 10.1016/j.jhydrol.2022.128323
200. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
201. 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
202. Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley–Leverett problem R. Rodriguez-Torrado et al. 10.1038/s41598-022-11058-2
203. Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks S. Chidepudi et al. 10.1016/j.ejrh.2023.101632
204. Cross-border impacts of cascade reservoirs on the temperature of the Lancang-Mekong river J. Wang et al. 10.1016/j.ecolind.2024.111918
205. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
206. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
207. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021
208. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
209. 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
210. 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
211. Predicting River Discharge in the Niger River Basin: A Deep Learning Approach S. Ogunjo et al. 10.3390/app14010012
212. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
213. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues 10.1016/j.jhydrol.2021.126674
214. FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction A. Kazadi et al. 10.1017/eds.2024.19
215. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
216. 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
217. Deep learning modeling framework for multi-resolution streamflow generation F. Custodio Pereira do Carmo et al. 10.2166/wcc.2024.706
218. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
219. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
220. Physics-guided machine learning from simulated data with different physical parameters S. Chen et al. 10.1007/s10115-023-01864-z
221. Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin J. Duque et al. 10.1063/5.0201784
222. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. 10.1016/j.jhydrol.2024.131774
223. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
224. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
225. 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
226. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
227. Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation X. Chen et al. 10.1016/j.jhydrol.2022.128562
228. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
229. 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
230. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
231. Predicting runoff series in ungauged basins of the Brazilian Cerrado biome D. Althoff et al. 10.1016/j.envsoft.2022.105315
232. Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model T. Nan et al. 10.1016/j.jhydrol.2023.130085
233. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
234. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
235. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
236. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
237. Machine Learning in Assessing the Performance of Hydrological Models E. Rozos et al. 10.3390/hydrology9010005
238. 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
239. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
240. A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al. 10.5194/hess-26-5163-2022
241. RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat R. Riggs et al. 10.1016/j.envsoft.2021.105254
242. Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm Y. Jhong et al. 10.1007/s11269-023-03713-8
243. Water Level Prediction and Forecasting Using a Long Short-Term Memory Model for Nam Ngum River Basin in Lao PDR C. Kim et al. 10.3390/w16131777
244. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell 10.1002/wat2.1530
245. Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework H. Llauca et al. 10.1016/j.ejrh.2023.101381
246. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
247. 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
248. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
249. Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths Y. Zhou et al. 10.1029/2022WR033214
250. 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
251. 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
252. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
253. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani 10.1038/s41597-025-04389-y
254. 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
255. Parsimonious statistical learning models for low-flow estimation J. Laimighofer et al. 10.5194/hess-26-129-2022
256. AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks F. Alqahtani 10.2166/wcc.2024.242
257. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
258. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
259. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
260. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
261. 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
262. Cyberinfrastructure for sustainability sciences C. Song et al. 10.1088/1748-9326/acd9dd
263. Influence of cascade reservoir operation in the Upper Mekong River on the general hydrological regime: A combined data-driven modeling approach X. Yuan et al. 10.1016/j.jenvman.2022.116339
264. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al. 10.1038/s43247-024-01594-2
265. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning M. Khan & R. Maity 10.1007/s11269-024-03735-w
266. 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
267. 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
268. Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats J. Liu et al. 10.5194/gmd-16-1553-2023
269. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
270. Persistent neural calibration for discharges modelling in drought-stressed catchments I. Pulido-Calvo et al. 10.1016/j.eswa.2024.123785
271. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
272. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. 10.1016/j.ecoinf.2025.102994
273. 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
274. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
275. 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
276. 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
277. An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input Z. Yao et al. 10.1016/j.jhydrol.2023.129977
278. Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al. 10.1007/s00500-024-09637-8
279. Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann 10.5194/nhess-23-3065-2023
280. Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
281. Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves M. Khan & R. Maity 10.1007/s00704-022-04103-7
282. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al. 10.1007/s11356-023-29522-5
283. 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
284. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
285. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
286. Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. 10.1029/2021WR031808
287. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell 10.1016/j.envsoft.2021.105113
288. Potential of the Coupled WRF/WRF-Hydro Modeling System for Flood Forecasting in the Ouémé River (West Africa) G. Quenum et al. 10.3390/w14081192
289. 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
290. Flood Uncertainty Estimation Using Deep Ensembles P. Chaudhary et al. 10.3390/w14192980
291. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
292. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
293. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
294. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
295. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
296. Long-Lead Forecasting of Runoff Season Flows in the Colorado River Basin Using a Random Forest Approach D. Woodson et al. 10.1061/JWRMD5.WRENG-6167
297. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
298. Deep learning, hydrological processes and the uniqueness of place K. Beven 10.1002/hyp.13805
299. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al. 10.1007/s00521-022-07523-8
300. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al. 10.3390/rs13122272
301. 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
302. Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions C. He et al. 10.3390/w13223294
303. Training machine learning with physics-based simulations to predict 2D soil moisture fields in a changing climate E. Leonarduzzi et al. 10.3389/frwa.2022.927113
304. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
305. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
306. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
307. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
308. Effects of cascading reservoirs on streamflow and sediment load with machine learning reconstructed time series in the upper Yellow River basin J. Fan et al. 10.1016/j.catena.2023.107008
309. Quantifying multi-year hydrological memory with Catchment Forgetting Curves A. de Lavenne et al. 10.5194/hess-26-2715-2022
310. Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget N. Martin 10.3390/hydrology10060129
311. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
312. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
313. 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
314. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
315. 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
316. Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
317. Comparison of Deep Learning Techniques for River Streamflow Forecasting X. Le et al. 10.1109/ACCESS.2021.3077703
318. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
319. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
320. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
321. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
322. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al. 10.1002/esp.5984
323. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
324. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions H. Tao et al. 10.1016/j.engappai.2023.107559
325. Mineral Prospectivity Mapping via Gated Recurrent Unit Model B. Yin et al. 10.1007/s11053-021-09979-2
326. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
327. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration M. Demirel et al. 10.3390/w11102083
328. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
329. Machine learning and artificial intelligence to aid climate change research and preparedness C. Huntingford et al. 10.1088/1748-9326/ab4e55
330. Physically-based modelling of UK river flows under climate change B. Smith et al. 10.3389/frwa.2024.1468855
331. 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
332. Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology L. De la Fuente et al. 10.1029/2021WR031548
333. Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest U.S A. Talib et al. 10.1016/j.jhydrol.2021.126579
334. Exploring the best sequence LSTM modeling architecture for flood prediction W. Li et al. 10.1007/s00521-020-05334-3
335. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction J. Choi et al. 10.3390/w14182910
336. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
337. Using Convolutional Neural Networks for Streamflow Projection in California S. Duan et al. 10.3389/frwa.2020.00028
338. Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins F. Rahmani et al. 10.1002/hyp.14400
339. 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
340. Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall–snowmelt regime Munzur River, Türkiye O. Katipoğlu 10.1007/s11356-023-29220-2
341. Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam's Digital Razor S. Weijs & B. Ruddell 10.1029/2019WR026471
342. 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
343. Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models V. Gholami et al. 10.1007/s11356-022-24347-0