267 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. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
6. 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
7. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
8. 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
9. 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
10. Machine-learning methods for stream water temperature prediction M. Feigl et al. 10.5194/hess-25-2951-2021
11. 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
12. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
13. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
14. 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
15. 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
16. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
17. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
18. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
19. 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
20. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
21. A Multilayer Perceptron Model for Stochastic Synthesis E. Rozos et al. 10.3390/hydrology8020067
22. 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
23. 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
24. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
33. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
34. 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
35. 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
36. Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms A. Ivanov et al. 10.24057/2071-9388-2022-087
37. 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
38. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
39. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
40. 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
41. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
42. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
43. 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
44. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
45. Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
46. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
47. 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
48. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
49. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
50. 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
51. 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
52. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al. 10.1007/s11356-021-17084-3
53. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
54. 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
55. 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
56. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
57. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
58. Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
59. 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
60. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
61. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
62. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965
63. Towards a comprehensive uncertainty assessment in environmental research and decision support P. Reichert 10.2166/wst.2020.032
64. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
65. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al. 10.1175/JHM-D-20-0072.1
66. 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
67. 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
68. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
69. Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms Y. Sun et al. 10.1002/ird.2942
70. 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
71. 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
72. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
73. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
74. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
75. 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
76. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
77. 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
78. 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
79. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
80. 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
81. Global soil moisture data derived through machine learning trained with in-situ measurements S. O. & R. Orth 10.1038/s41597-021-00964-1
82. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
83. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
84. 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
85. Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies A. DelSanto et al. 10.3390/w15152813
86. 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
87. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
88. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
89. 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
90. 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
91. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
92. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
93. 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
94. High-frequency data significantly enhances the prediction ability of point and interval estimation X. Liu et al. 10.1016/j.scitotenv.2023.169289
95. 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
96. 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
97. Snowmelt-Driven Streamflow Prediction Using Machine Learning Techniques (LSTM, NARX, GPR, and SVR) S. Thapa et al. 10.3390/w12061734
98. 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
99. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
100. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al. 10.1016/j.envsoft.2021.105112
101. Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al. 10.1016/j.jenvman.2023.118744
102. 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
103. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al. 10.1038/s41598-023-35963-2
104. Regionalisierung hydrologischer Modelle mit Function Space Optimization M. Feigl et al. 10.1007/s00506-021-00766-0
105. 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
106. 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
107. A coupled model applied to complex river–lake systems Q. Zhang et al. 10.1080/02626667.2023.2285441
108. 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
109. Deep learning model on rates of change for multi-step ahead streamflow forecasting W. Tan et al. 10.2166/hydro.2023.001
110. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems A. Rupe et al. 10.1088/1367-2630/ac95b7
111. Issues in generating stochastic observables for hydrological models K. Beven 10.1002/hyp.14203
112. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
113. 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
114. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
115. 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
116. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
117. 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
118. 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
119. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks A. Abbas et al. 10.1016/j.jhydrol.2020.125370
120. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
121. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
122. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
123. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
124. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
125. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
126. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al. 10.3389/frwa.2022.886964
127. 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
128. 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
129. 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
130. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
131. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
132. 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
133. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
134. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
135. 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
136. Classification and Zoning of Rivers by Their Water Regime: History, Methodology, and Perspectives N. Frolova et al. 10.1134/S0097807821020056
137. Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al. 10.3390/ijgi12110464
138. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
139. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
140. 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
141. 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
142. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
143. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
144. Artificial intelligence can provide accurate forecasts of extreme floods at global scale 10.1038/d41586-024-00835-w
145. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin S. Nimai et al. 10.3390/w15213758
146. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
147. 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
148. 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
149. 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
150. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
151. 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
152. 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
153. Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks S. Chidepudi et al. 10.1016/j.ejrh.2023.101632
154. Cross-border impacts of cascade reservoirs on the temperature of the Lancang-Mekong river J. Wang et al. 10.1016/j.ecolind.2024.111918
155. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
156. 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
157. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
158. 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
159. 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
160. Predicting River Discharge in the Niger River Basin: A Deep Learning Approach S. Ogunjo et al. 10.3390/app14010012
161. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
162. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues 10.1016/j.jhydrol.2021.126674
163. 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
164. 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
165. Deep learning modeling framework for multi-resolution streamflow generation F. Custodio Pereira do Carmo et al. 10.2166/wcc.2024.706
166. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
167. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
168. Physics-guided machine learning from simulated data with different physical parameters S. Chen et al. 10.1007/s10115-023-01864-z
169. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
170. 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
171. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
172. 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
173. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
174. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
175. Predicting runoff series in ungauged basins of the Brazilian Cerrado biome D. Althoff et al. 10.1016/j.envsoft.2022.105315
176. 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
177. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
178. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
179. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
180. Machine Learning in Assessing the Performance of Hydrological Models E. Rozos et al. 10.3390/hydrology9010005
181. 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
182. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
183. 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
184. RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat R. Riggs et al. 10.1016/j.envsoft.2021.105254
185. Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm Y. Jhong et al. 10.1007/s11269-023-03713-8
186. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell 10.1002/wat2.1530
187. 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
188. 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
189. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
190. Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths Y. Zhou et al. 10.1029/2022WR033214
191. 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
192. 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
193. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
194. 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
195. Parsimonious statistical learning models for low-flow estimation J. Laimighofer et al. 10.5194/hess-26-129-2022
196. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
197. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
198. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
199. 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
200. Cyberinfrastructure for sustainability sciences C. Song et al. 10.1088/1748-9326/acd9dd
201. 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
202. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning M. Khan & R. Maity 10.1007/s11269-024-03735-w
203. 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
204. 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
205. 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
206. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
207. Persistent neural calibration for discharges modelling in drought-stressed catchments I. Pulido-Calvo et al. 10.1016/j.eswa.2024.123785
208. 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
209. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
210. 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
211. 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
212. 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
213. Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann 10.5194/nhess-23-3065-2023
214. 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
215. 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
216. 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
217. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
218. Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. 10.1029/2021WR031808
219. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell 10.1016/j.envsoft.2021.105113
220. 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
221. 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
222. Flood Uncertainty Estimation Using Deep Ensembles P. Chaudhary et al. 10.3390/w14192980
223. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
224. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
225. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
226. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
227. 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
228. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
229. Deep learning, hydrological processes and the uniqueness of place K. Beven 10.1002/hyp.13805
230. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al. 10.1007/s00521-022-07523-8
231. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al. 10.3390/rs13122272
232. 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
233. 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
234. 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
235. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
236. 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
237. Quantifying multi-year hydrological memory with Catchment Forgetting Curves A. de Lavenne et al. 10.5194/hess-26-2715-2022
238. Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget N. Martin 10.3390/hydrology10060129
239. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
240. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
241. 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
242. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
243. 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
244. Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
245. Comparison of Deep Learning Techniques for River Streamflow Forecasting X. Le et al. 10.1109/ACCESS.2021.3077703
246. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
247. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
248. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
249. 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
250. Mineral Prospectivity Mapping via Gated Recurrent Unit Model B. Yin et al. 10.1007/s11053-021-09979-2
251. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
252. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration . Demirel et al. 10.3390/w11102083
253. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
254. Machine learning and artificial intelligence to aid climate change research and preparedness C. Huntingford et al. 10.1088/1748-9326/ab4e55
255. 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
256. Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology L. De la Fuente et al. 10.1029/2021WR031548
257. 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
258. Investigation of Hyperparameter Setting of a Long Short-Term Memory Model Applied for Imputation of Missing Discharge Data of the Daihachiga River . Weilisi & T. Kojima 10.3390/w14020213
259. Exploring the best sequence LSTM modeling architecture for flood prediction W. Li et al. 10.1007/s00521-020-05334-3
260. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction J. Choi et al. 10.3390/w14182910
261. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
262. Using Convolutional Neural Networks for Streamflow Projection in California S. Duan et al. 10.3389/frwa.2020.00028
263. 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
264. 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
265. 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
266. 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
267. 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