375 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. Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting X. Cheng & J. Ma 10.1038/s41598-025-92995-6
10. 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
11. 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
12. Machine-learning methods for stream water temperature prediction M. Feigl et al. 10.5194/hess-25-2951-2021
13. 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
14. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
15. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
16. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
17. 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
18. 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
19. 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
20. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
21. 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
22. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
23. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
24. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. 10.5194/hess-29-1277-2025
25. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. 10.1017/eds.2024.14
26. 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
27. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
28. Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain S. Asadi et al. 10.1016/j.envsoft.2025.106360
29. Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models W. Armstrong et al. 10.1080/02626667.2025.2471430
30. A Multilayer Perceptron Model for Stochastic Synthesis E. Rozos et al. 10.3390/hydrology8020067
31. 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
32. 长江流域洞庭湖区出入湖磷通量模拟及水质预测：机器学习与传统水文模型耦合方法 J. Liu et al. 10.3799/dqkx.2024.061
33. 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
34. 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
35. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
43. 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
44. 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
45. 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
46. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
47. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
48. 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
49. 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
50. 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
51. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
52. 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
53. Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms A. Ivanov et al. 10.24057/2071-9388-2022-087
54. Advancing the science of headwater streamflow for global water protection H. Golden et al. 10.1038/s44221-024-00351-1
55. 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
56. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
57. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
58. 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
59. 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
60. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
61. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
62. 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
63. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
64. 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
65. 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
66. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
67. 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
68. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
69. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
70. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
71. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
72. 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
73. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
74. 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
75. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al. 10.1007/s11356-021-17084-3
76. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
77. Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. 10.5194/hess-28-5443-2024
78. 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
79. 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
80. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
81. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
82. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
83. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
84. VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting J. Liu et al. 10.1016/j.jhydrol.2025.132769
85. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
86. 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
87. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
88. 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
89. Early warning of complex climate risk with integrated artificial intelligence M. Reichstein et al. 10.1038/s41467-025-57640-w
90. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
91. 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
92. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
93. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965
94. Towards a comprehensive uncertainty assessment in environmental research and decision support P. Reichert 10.2166/wst.2020.032
95. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
96. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al. 10.1175/JHM-D-20-0072.1
97. 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
98. 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
99. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
100. Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms Y. Sun et al. 10.1002/ird.2942
101. 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
102. 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
103. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
104. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
105. 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
106. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
107. 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
108. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
109. Exploring the influence of training sampling strategies on time-series deep learning model in hydrology S. Yoon & K. Ahn 10.1016/j.jhydrol.2025.132774
110. 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
111. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan 10.5194/hess-29-785-2025
112. 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
113. Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al. 10.1016/j.envsoft.2025.106374
114. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
115. 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
116. Global soil moisture data derived through machine learning trained with in-situ measurements S. O. & R. Orth 10.1038/s41597-021-00964-1
117. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
118. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
119. 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
120. 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
121. Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies A. DelSanto et al. 10.3390/w15152813
122. 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
123. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
124. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
125. 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
126. 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
127. 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
128. Classification-enhanced LSTM model for predicting river water levels J. Luo et al. 10.1016/j.jhydrol.2024.132535
129. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
130. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
131. 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
132. 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
133. High-frequency data significantly enhances the prediction ability of point and interval estimation X. Liu et al. 10.1016/j.scitotenv.2023.169289
134. 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
135. 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
136. Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al. 10.1002/hyp.15270
137. In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models A. Abbas et al. 10.5194/hess-25-6185-2021
138. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
139. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al. 10.1016/j.envsoft.2021.105112
140. Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al. 10.1016/j.jenvman.2023.118744
141. A parsimonious setup for streamflow forecasting using CNN-LSTM S. Pokharel & T. Roy 10.2166/hydro.2024.114
142. 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
143. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al. 10.1038/s41598-023-35963-2
144. Mitigating flood risk at Shah Alam, Malaysia for sustainable development A. Leeonis et al. 10.1007/s43621-024-00504-y
145. Regionalisierung hydrologischer Modelle mit Function Space Optimization M. Feigl et al. 10.1007/s00506-021-00766-0
146. 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
147. 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
148. 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
149. A coupled model applied to complex river–lake systems Q. Zhang et al. 10.1080/02626667.2023.2285441
150. 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
151. Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations B. Yu et al. 10.1016/j.jhydrol.2025.133052
152. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
153. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
154. 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
155. A novel operational water quality mobile prediction system with LSTM-Seq2Seq model L. Xie et al. 10.1016/j.envsoft.2024.106290
156. 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
157. 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
158. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
159. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
160. 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
161. Deep learning model on rates of change for multi-step ahead streamflow forecasting W. Tan et al. 10.2166/hydro.2023.001
162. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. 10.3390/w17030339
163. Improving prediction of class-imbalanced time series through curation of training data: A case study of frozen ground prediction M. Ghosh et al. 10.1016/j.hydroa.2025.100201
164. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems A. Rupe et al. 10.1088/1367-2630/ac95b7
165. Issues in generating stochastic observables for hydrological models K. Beven 10.1002/hyp.14203
166. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
167. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
168. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
169. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
170. 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
171. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
172. Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation N. Martin & J. White 10.3390/w16192758
173. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
174. 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
175. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
176. 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
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