250 citations as recorded by crossref.

1. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis Q. Tian et al. https://doi.org/10.3390/w15183184
2. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1749-2025
3. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. https://doi.org/10.1016/j.eswa.2024.126004
4. Short-term dissolved oxygen forecasting in lakes of the middle and lower Yangtze River basin using generative AI–enhanced machine learning W. Gu et al. https://doi.org/10.1016/j.ejrh.2026.103338
5. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. https://doi.org/10.2166/nh.2024.003
6. Comparative Analysis of LSTM and Random Forest Algorithms for Streamflow Prediction: A Case Study of Diverse River Basins in the United States A. Shanko & A. Melesse https://doi.org/10.3390/w18141768
7. Evaluation of the hydrological utility of the GPM IMERG satellite precipitation products H. Meng & T. Zhao https://doi.org/10.1016/j.atmosres.2025.108139
8. AI‐based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada P. Parisouj et al. https://doi.org/10.1111/1752-1688.13098
9. Prediction of mine water quality by the Seq2Seq model based on attention mechanism X. Wang & Y. Li https://doi.org/10.1016/j.heliyon.2024.e37916
10. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić https://doi.org/10.3389/frwa.2022.934709
11. Combining recurrent neural networks with variational mode decomposition and multifractals to predict rainfall time series H. Zhou et al. https://doi.org/10.5194/hess-29-4437-2025
12. Operational low-flow forecasting using LSTMs J. Deng et al. https://doi.org/10.3389/frwa.2023.1332678
13. RETRACTED ARTICLE: Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al. https://doi.org/10.1007/s00500-024-09637-8
14. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. https://doi.org/10.1111/1752-1688.12964
15. FlowGATFormer: A streamflow prediction model based on spatiotemporal dual attention F. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135771
16. Explainable Deep Ensemble Bias Correction of GloFAS-ERA5 Streamflow Across Snow-Influenced Transboundary Basins of Central Asia T. Honcharenko et al. https://doi.org/10.3390/w18162055
17. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. https://doi.org/10.21105/joss.04050
18. An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. https://doi.org/10.1016/j.jhydrol.2022.127659
19. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. https://doi.org/10.3390/w17060907
20. MEDAL: a mechanism-embedded dual-physics attention learning framework for snowmelt-driven runoff prediction in cold-region basins M. Gu et al. https://doi.org/10.1016/j.jhydrol.2026.136376
21. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. https://doi.org/10.1016/j.jhydrol.2024.131438
22. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. https://doi.org/10.1002/hyp.14847
23. Evaluating Grazing Management for Drought Reduction Under Different Climate Change Scenarios M. Abdulahi et al. https://doi.org/10.3390/cli14040086
24. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia https://doi.org/10.1016/j.jhydrol.2023.130421
25. A Runoff Prediction Model Based on Nonhomogeneous Markov Chain W. Li et al. https://doi.org/10.1007/s11269-022-03091-7
26. MSTRFormer: A multi-factor daily runoff forecasting model integrating adaptive graph convolution and linear attention W. Wang et al. https://doi.org/10.1016/j.asoc.2026.115302
27. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi https://doi.org/10.1016/j.jhydrol.2024.131835
28. The ability of LSTM to model snowmelt versus rainfall generated floods S. Bakke et al. https://doi.org/10.5194/hess-30-6207-2026
29. StreamFormer: A multi-resolution transformer for scalable global river streamflow prediction J. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136505
30. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan https://doi.org/10.1029/2022WR033091
31. Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China L. Xing et al. https://doi.org/10.1016/j.compag.2023.108139
32. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. https://doi.org/10.5194/hess-29-1061-2025
33. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. https://doi.org/10.2166/hydro.2023.228
34. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al. https://doi.org/10.1002/esp.5984
35. A Comprehensive Comparative Analysis of Machine Learning Models for Daily Precipitation Forecasting Using Satellite-Based Meteorological Data T. Müftüoğlu https://doi.org/10.47495/okufbed.1799905
36. Reducing market risk in run-of-river hydropower: The economic value of improving predictions of day-ahead production K. Wergeland et al. https://doi.org/10.1016/j.renene.2026.126147
37. Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China T. Zhang et al. https://doi.org/10.1016/j.ecolind.2024.112715
38. Hydrology-informed machine learning for enhancing surface runoff elucidation P. Hua et al. https://doi.org/10.1016/j.scitotenv.2025.179936
39. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider https://doi.org/10.1029/2022WR032123
40. PatchX: A dual-branch embedding framework with cross-attention for streamflow forecasting J. Jiang et al. https://doi.org/10.1016/j.envsoft.2026.107176
41. Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm S. Liao et al. https://doi.org/10.1007/s11269-023-03442-y
42. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. https://doi.org/10.2166/hydro.2024.268
43. Forecasting Multi-Step Soil Moisture with Three-Phase Hybrid Wavelet-Least Absolute Shrinkage Selection Operator-Long Short-Term Memory Network (moDWT-Lasso-LSTM) Model W. Jayasinghe et al. https://doi.org/10.3390/w16213133
44. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. https://doi.org/10.1016/j.ejrh.2022.101119
45. A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff C. Xia et al. https://doi.org/10.1038/s41598-025-32088-6
46. 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. https://doi.org/10.1007/s11356-023-29522-5
47. Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan C. Hsu & Y. Lin https://doi.org/10.1016/j.agwat.2024.109178
48. 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 https://doi.org/10.5194/hess-28-479-2024
49. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. https://doi.org/10.5194/hess-26-5085-2022
50. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés https://doi.org/10.1016/j.jhydrol.2024.132569
51. A downscaling-fusion framework based on machine learning to improve daily precipitation estimates in the dry-hot valley of southwest China J. Feng & B. Liu https://doi.org/10.1016/j.ejrh.2026.103537
52. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn https://doi.org/10.1016/j.jenvman.2024.122114
53. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al. https://doi.org/10.1038/s41597-025-05612-6
54. Multi-Objective Hyperparameter Optimization Improves the Interpretability of LSTM Rainfall–Runoff Models Q. Tan et al. https://doi.org/10.3390/hydrology13080218
55. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al. https://doi.org/10.3390/rs13122272
56. Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction X. Wang et al. https://doi.org/10.3390/w16081179
57. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. https://doi.org/10.1016/j.ejrh.2024.101776
58. Urban Rainfall Runoff Prediction Using LSTM with DTW-Enhanced Clustering Y. Tian et al. https://doi.org/10.1061/JHYEFF.HEENG-6450
59. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. https://doi.org/10.1007/s00506-021-00767-z
60. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić https://doi.org/10.5194/hess-26-795-2022
61. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. https://doi.org/10.5194/hess-28-4187-2024
62. Application of Deep Learning and Rain‐On‐Grid Hydrodynamic Modeling for Catchment‐Scale Flood Forecasting in Guwahati, India S. Ashok & S. Pekkat https://doi.org/10.1111/jfr3.70186
63. Simulating the climate driven runoff in data-scarce mountains by machine learning and downscaling reanalysis data M. Fan et al. https://doi.org/10.1007/s00477-022-02231-0
64. Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation L. Lang et al. https://doi.org/10.1016/j.jhydrol.2025.132806
65. WITHDRAWN: From Climate Signals to Smart Predictions: Advanced Machine Learning and Deep Learning for Hydrological Modeling in Data-Scarce Bangladesh M. Islam et al. https://doi.org/10.1016/j.htopen.2026.100005
66. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework L. Zhang et al. https://doi.org/10.3390/atmos15020164
67. Attributing extreme rainfall–runoff dynamics in karst watersheds: An LSTM-based mechanistic modeling and interpretation framework Z. He et al. https://doi.org/10.1016/j.advwatres.2026.105293
68. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2024.132269
69. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. https://doi.org/10.3390/w16101407
70. Simulación del caudal en España utilizando redes neuronales Long Short-Term Memory J. Casado-Rodríguez et al. https://doi.org/10.4995/ia.25084
71. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
72. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang https://doi.org/10.1016/j.jhydrol.2023.129946
73. 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. https://doi.org/10.3390/w15030505
74. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al. https://doi.org/10.1016/j.envsoft.2023.105730
75. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. https://doi.org/10.1029/2022WR033918
76. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij https://doi.org/10.1007/s00477-022-02276-1
77. Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning J. Choi et al. https://doi.org/10.4491/KSEE.2024.46.10.602
78. Multi-station rainfall–runoff–operation model for assessing flood characteristics in a cascading dam system under the impacts of climate change and sediment dynamics P. Nguyen et al. https://doi.org/10.1016/j.jhydrol.2026.135929
79. Study on the Optimization and Stability of Machine Learning Runoff Prediction Models in the Karst Area C. Mo et al. https://doi.org/10.3390/app12104979
80. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. https://doi.org/10.5194/hess-26-3377-2022
81. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. https://doi.org/10.1029/2021WR031751
82. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar https://doi.org/10.1016/j.advwatres.2024.104842
83. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. https://doi.org/10.1002/rvr2.72
84. Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system M. Vu et al. https://doi.org/10.1016/j.scitotenv.2023.165494
85. Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models S. Wang et al. https://doi.org/10.1007/s11269-022-03305-y
86. Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models S. Khorram & N. Jehbez https://doi.org/10.1080/19942060.2024.2387051
87. A Hybrid Muskingum–Machine Learning Flood Forecasting Model: Application and Evaluation in the Tarim River Basin P. Wang et al. https://doi.org/10.3390/w18091077
88. Learning water diversion behaviour in regulated river networks using graph neural networks and neural decision trees M. Wirthensohn et al. https://doi.org/10.1016/j.jhydrol.2026.136350
89. From climate signals to smart predictions: advanced machine learning and deep learning for hydrological modeling in data-scarce Bangladesh M. Islam et al. https://doi.org/10.2166/h2oj.2026.036
90. Building urban disaster resilience through AI solutions: A human-centric decision framework for adopting efficient combination of solutions M. Yeganegi et al. https://doi.org/10.1016/j.pdisas.2026.100770
91. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. https://doi.org/10.1016/j.jhydrol.2024.131986
92. Improving extreme streamflow prediction by integrating CEEMDAN, autoencoders, and attention-enhanced BiLSTM optimized via GSA-PSO J. Sha et al. https://doi.org/10.1016/j.envsoft.2026.107041
93. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al. https://doi.org/10.3390/rs14102488
94. Advanced artificial intelligence and remote sensing for earth observation–based tree crop monitoring: A global review with african perspectives A. Moussaid et al. https://doi.org/10.1016/j.neucom.2026.134165
95. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. https://doi.org/10.5194/hess-26-5493-2022
96. A precipitation-runoff swift simulation model dedicated to emergency response to flood prediction Z. Yu et al. https://doi.org/10.1007/s12665-022-10604-5
97. Long-term inflow forecast using meteorological data based on long short-term memory neural networks H. Zhao et al. https://doi.org/10.2166/hydro.2024.196
98. A hyperparameter tuning strategy for an LSTM model to simulate reservoir outflows: large-scale evaluation across 441 dams in the CONUS M. Basirifard et al. https://doi.org/10.1016/j.jhydrol.2026.136204
99. A Hybrid Rank-Preserving and Evolutionary Algorithm for Multisite Daily Streamflow Simulation S. Pitulić et al. https://doi.org/10.3390/a19070541
100. Multivariate time series forecasting with a numerical spiking neural P system-inspired network J. Xu et al. https://doi.org/10.1016/j.neucom.2026.134476
101. Integrative statistical analysis of compound flood dynamics under climate variability in China’s coastal zone T. Atmaja & D. Lee https://doi.org/10.1016/j.jhydrol.2026.135284
102. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. https://doi.org/10.1029/2021WR030993
103. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. https://doi.org/10.1002/hyp.14837
104. A novel integrated multicriteria decision analysis framework for group decision-making in evapotranspiration model selection D. Awhari et al. https://doi.org/10.1007/s10668-024-05946-y
105. Quantum-enhanced deep learning for severe weather prediction: a 10-qubit QCNN-LSTM for bow echo forecasting H. Canot et al. https://doi.org/10.3934/bdia.2025008
106. Autoencoder-guided flood-event classification and diffusion augmentation for event-scale flood forecasting in a data-scarce basin T. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.136809
107. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
108. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. https://doi.org/10.5194/hess-28-3051-2024
109. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al. https://doi.org/10.3390/w15203626
110. Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models A. Amini et al. https://doi.org/10.1016/j.jhydrol.2024.130804
111. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn https://doi.org/10.1016/j.jhydrol.2024.130862
112. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. https://doi.org/10.21105/joss.04237
113. The optimization path of smart ice and snow sports tourism industry development under hybrid neural network model Y. Sun et al. https://doi.org/10.1038/s41598-026-47099-0
114. Analytic outlet-state identification in a pathway-age neural transport operator enables cross-domain streamflow forecasting in human-affected basins X. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136639
115. Evaluation of temporal fusion transformers for streamflow forecasting: effects of input configuration and cross-basin performance M. Mohan et al. https://doi.org/10.1080/02626667.2026.2736761
116. Assessing the stability of LSTM runoff projections in Switzerland under climate scenarios F. Courvoisier et al. https://doi.org/10.5194/hess-30-5873-2026
117. From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta https://doi.org/10.1016/j.envsoft.2025.106696
118. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. https://doi.org/10.3389/frwa.2023.1166124
119. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al. https://doi.org/10.1016/j.ejrh.2026.103299
120. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi https://doi.org/10.5194/hess-26-2387-2022
121. Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al. https://doi.org/10.1088/1748-9326/ae49a2
122. Fully differentiable, fully distributed rainfall-runoff modeling F. Scholz et al. https://doi.org/10.5194/hess-29-6257-2025
123. A dynamic-gated Mixture-of-Experts framework improves and interprets daily streamflow simulation W. Yuan et al. https://doi.org/10.1038/s43247-026-03799-z
124. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. https://doi.org/10.5194/hess-27-139-2023
125. Machine-Learning Projections of Future Streamflow in Four Central High Atlas Basins (Morocco): Multi-Scale Drying, Extremes, and Implications for Hassan 1st Dam Inflows A. Zilali et al. https://doi.org/10.1016/j.rcar.2026.10.004
126. Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology Q. Wang et al. https://doi.org/10.1016/j.mlwa.2024.100584
127. Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN) D. Cao et al. https://doi.org/10.3390/computation11020039
128. Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery L. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134749
129. Simple Box-Cox probabilistic models for hourly streamflow predictions C. Prieto et al. https://doi.org/10.5194/hess-30-6907-2026
130. Runoff Prediction Method Based on Pangu-Weather W. Yang et al. https://doi.org/10.3390/w17091405
131. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al. https://doi.org/10.3390/w17182722
132. Impact of overlooked input structure on rainfall forecasting performances of LSTM and XGBoost models H. Tongal https://doi.org/10.1080/02626667.2026.2761613
133. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. https://doi.org/10.1029/2021WR029583
134. A Dual-Branch TCN–TE Model for Multi-Horizon Runoff Forecasting Using Multi-Station Hydrological Observations Z. Meng et al. https://doi.org/10.3390/su18115289
135. Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui https://doi.org/10.5194/hess-25-5839-2021
136. Flood forecasting with machine learning models in an operational framework S. Nevo et al. https://doi.org/10.5194/hess-26-4013-2022
137. Event-Aware Validation of Short-Term Water-Level Forecasting in a Single Data-Scarce Gauged Catchment: Implications for Local Flash-Flood Risk Management X. He et al. https://doi.org/10.3390/w18192424
138. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. https://doi.org/10.5194/hess-26-3537-2022
139. Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al. https://doi.org/10.1016/j.aei.2025.104152
140. A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique D. Kim et al. https://doi.org/10.3390/w15050972
141. Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al. https://doi.org/10.3390/w15152760
142. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al. https://doi.org/10.1016/j.ejrh.2024.101720
143. Transition to transpiration-dominated evapotranspiration on the Loess Plateau: spatially divergent driving mechanism and threshold effect after two decades of reforestation Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2026.135191
144. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2023.129956
145. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al. https://doi.org/10.1016/j.ejrh.2024.102062
146. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. https://doi.org/10.5194/npg-31-535-2024
147. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan https://doi.org/10.5194/hess-29-785-2025
148. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. https://doi.org/10.1007/s11269-022-03076-6
149. A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. https://doi.org/10.1007/s11269-023-03583-0
150. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. https://doi.org/10.1029/2022WR034352
151. Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China H. Xue et al. https://doi.org/10.1016/j.ejrh.2024.102100
152. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. https://doi.org/10.5194/hess-26-4407-2022
153. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez https://doi.org/10.1016/j.ejrh.2025.102719
154. FKDM: a frequency-guided Koopman dynamics model with heterogeneous mixture-of-experts for robust daily runoff forecasting under non-stationarity W. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.136184
155. Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China Y. Wang et al. https://doi.org/10.1007/s11356-022-22536-5
156. Analysis of data cleansing methods for improving meteorological data quality: a case study M. Rahman & M. Khan https://doi.org/10.1007/s12145-024-01608-9
157. A Deep Reinforcement Learning Framework for Cascade Reservoir Operations Under Runoff Uncertainty J. Xu et al. https://doi.org/10.3390/w17152324
158. Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning Z. Zhang et al. https://doi.org/10.1371/journal.pone.0346237
159. Mining relevant partial periodic pattern of multi-source time series data Y. Xun et al. https://doi.org/10.1016/j.ins.2022.10.049
160. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. https://doi.org/10.3390/rs14030698
161. Causality-Structured Deep Learning for Soil Moisture Predictions L. Li et al. https://doi.org/10.1175/JHM-D-21-0206.1
162. Advancements in rainfall-runoff prediction: Exploring state-of-the-art neural computing modeling approaches D. Irwan et al. https://doi.org/10.1016/j.aej.2025.02.060
163. A novel hybrid framework for combining process-based models with machine learning for streamflow prediction X. Jiang et al. https://doi.org/10.1016/j.advwatres.2025.105177
164. Technical note: Benchmarking large-domain model performance under sampling uncertainty G. Gründemann et al. https://doi.org/10.5194/hess-30-3439-2026
165. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. https://doi.org/10.1038/s41597-022-01785-6
166. Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study H. Yang & W. Li https://doi.org/10.1007/s11269-022-03389-6
167. The Application of Artificial Intelligence in Streamflow Forecasting: A Review S. Eswara et al. https://doi.org/10.1007/s11269-026-04883-x
168. WRO-water: A hydrology-guided multi-head attention transformer for runoff prediction and water quality early warning Q. Abbas et al. https://doi.org/10.1016/j.jenvman.2026.130545
169. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al. https://doi.org/10.1016/j.ejrh.2025.102998
170. Evaluating data-driven models to estimate snow water equivalent in the Sierra Nevada E. Sthapit et al. https://doi.org/10.1016/j.ejrh.2026.103481
171. Federated learning based water streamflow forecasting via multi-sensor data fusion Y. Xia et al. https://doi.org/10.1016/j.inffus.2025.104020
172. Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation J. Ougahi & J. Rowan https://doi.org/10.1038/s41598-025-87187-1
173. Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control R. Song et al. https://doi.org/10.3390/su18147494
174. Temporal inductive biases in hourly flood forecasting: a comparative analysis of recurrent, attention-based, and state-space neural networks B. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135727
175. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al. https://doi.org/10.1016/j.envsoft.2026.106897
176. Physics-informed gated transformer for robust flash flood forecasting in data-scarce small catchments via variance-constrained learning S. Wei & X. Wang https://doi.org/10.1016/j.jhydrol.2026.135706
177. 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. https://doi.org/10.5194/hess-25-5517-2021
178. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. https://doi.org/10.1016/j.jhydrol.2023.129990
179. Earth and Planetary Surface Processes Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science C. Burberry et al. https://doi.org/10.1029/2022EA002414
180. Scalable big earth observation data mining algorithms: a review N. Sisodiya et al. https://doi.org/10.1007/s12145-023-01032-5
181. Development of a Two-Stage LSTM for Multi-Step Runoff Forecasting Using a XAJ Model and EEMD Z. Yang et al. https://doi.org/10.1007/s11269-025-04420-2
182. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
183. Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany M. Weißenborn et al. https://doi.org/10.5194/hess-29-5131-2025
184. Streamflow forecast model using ANN D. Sonowal et al. https://doi.org/10.1080/23249676.2024.2310098
185. Evaluating machine learning and deep learning for upscaling instantaneous latent heat flux to daily scale E. Tunca https://doi.org/10.1007/s00271-026-01079-4
186. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. https://doi.org/10.5194/hess-26-5449-2022
187. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al. https://doi.org/10.1007/s11269-023-03552-7
188. Enhancing Prediction Accuracy and Data Handling for Environmental Applications in Innovative Modeling of Groundwater Level Fluctuations Based on the Tree Ensembles Technique D. Chi et al. https://doi.org/10.1061/JHYEFF.HEENG-6395
189. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al. https://doi.org/10.1016/j.envsoft.2026.106989
190. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.127431
191. Japanese waka translation supported by internet of things and artificial intelligence technology R. Shen https://doi.org/10.1038/s41598-025-85184-y
192. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta https://doi.org/10.3390/su141610447
193. A lag-informed deep learning framework for real-time downstream water-level forecasting and flood decision support in the Kalu Ganga Basin, Sri Lanka A. Senavirathne et al. https://doi.org/10.3389/frwa.2026.1968925
194. Flood modelling in a montane basin using machine learning, deep learning and sensor network data D. Ba & J. Langhammer https://doi.org/10.2166/hydro.2026.175
195. A Hybrid Linear and Nonlinear Hierarchical Reconciliation Method for Consistent Multi-timescale Precipitation Forecasting M. Yang et al. https://doi.org/10.1007/s11269-026-04780-3
196. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2024.132003
197. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al. https://doi.org/10.3390/hydrology12100261
198. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. https://doi.org/10.5194/hess-28-2107-2024
199. Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction S. Apak et al. https://doi.org/10.1038/s41598-026-40713-1
200. Daily streamflow prediction using lightGBM and random forest in the Fiumarella di Corleto basin, Southern Italy M. Yaseen et al. https://doi.org/10.1007/s40808-026-02816-6
201. Streamflow Estimation at Ungauged Sections in a Nested Basin Using an HEG-LSTM Model Y. He et al. https://doi.org/10.3390/w18172145
202. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al. https://doi.org/10.3390/w16192805
203. Evaluation of GPM IMERG products for estimating daily precipitation, detecting extreme precipitation and monitoring meteorological droughts in Australia H. Meng et al. https://doi.org/10.1080/02626667.2025.2564353
204. Entropy-MIMR-LSTM framework for rain gauge network optimization in mountainous small watersheds: A case study of Fuhuxi Watershed, China Y. Cui et al. https://doi.org/10.1007/s11629-025-0174-3
205. Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics R. Farooq et al. https://doi.org/10.1007/s41748-025-01020-3
206. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. https://doi.org/10.3390/w14050697
207. Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting Y. Tian et al. https://doi.org/10.1007/s12145-025-01907-9
208. Deep learning error post-processing improves stochastic watershed modeling B. Manoli et al. https://doi.org/10.1016/j.jhydrol.2026.135663
209. Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach R. Gupta et al. https://doi.org/10.1016/j.compag.2024.109355
210. INFO-LAX: an interpretable and uncertainty-aware framework for monthly rainfall erosivity estimation J. Yuan et al. https://doi.org/10.1016/j.jhydrol.2026.136687
211. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. https://doi.org/10.3390/w14111745
212. Evaluation of Transformer model and Self-Attention mechanism in the Yangtze River basin runoff prediction X. Wei et al. https://doi.org/10.1016/j.ejrh.2023.101438
213. Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation W. Li et al. https://doi.org/10.1038/s41598-024-62127-7
214. Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes R. Ahsan et al. https://doi.org/10.1016/j.imu.2022.100860
215. A Grid-Based Long Short-Term Memory Framework for Runoff Projection and Uncertainty in the Yellow River Source Area Under CMIP6 Climate Change H. Chu et al. https://doi.org/10.3390/w17050750
216. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. https://doi.org/10.1029/2021WR031065
217. Visualizing victory: employing GAF-transformed images and convolutional LSTM to predict soccer game strategies S. Khan et al. https://doi.org/10.1007/s00521-026-12452-x
218. Flood modelling and prediction using hydrologic similarity – driven parameter regionalization in ungauged catchments H. Lu et al. https://doi.org/10.2166/hydro.2025.036
219. Lowland transboundary river in a cold, semi-arid steppe: review of the Yesil River basin N. Ongdas et al. https://doi.org/10.1007/s12665-025-12500-0
220. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. https://doi.org/10.5194/hess-26-5793-2022
221. Benchmarking structured state space models for differentiable parameter learning: A large-scale evaluation on accuracy and physical consistency X. Jing et al. https://doi.org/10.1016/j.eswa.2026.133040
222. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. https://doi.org/10.1016/j.jhydrol.2022.128150
223. Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches V. Kartal et al. https://doi.org/10.1016/j.pce.2024.103757
224. Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting A. Amini et al. https://doi.org/10.1029/2022WR032789
225. Artificial intelligence for urban emergency management: A systematic review of urban data foundations and methodological pathways R. Qin et al. https://doi.org/10.1016/j.pdisas.2026.100713
226. Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model S. Gegenleithner et al. https://doi.org/10.5194/hess-29-1939-2025
227. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.128835
228. Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand P. Panyadee & P. Champrasert https://doi.org/10.3390/su16114433
229. Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data L. Wang et al. https://doi.org/10.3390/app132011151
230. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2025.133689
231. Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model H. Shen et al. https://doi.org/10.3390/w17243507
232. Prediction of storm surge in the Pearl River Estuary based on data-driven model Q. Tian et al. https://doi.org/10.3389/fmars.2024.1390364
233. Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media X. Zeng & Z. Zhong https://doi.org/10.4018/JOEUC.314786
234. An efficient parallel runoff forecasting model for capturing global and local feature information Y. Hong et al. https://doi.org/10.1038/s41598-025-96940-5
235. Exploring xLSTM for large-sample hourly rainfall–runoff modelling Q. Yu et al. https://doi.org/10.1016/j.envsoft.2026.107193
236. Uncertainty-aware improved LSTM flood forecasting in a snowmelt-dominated arid basin Y. Xiao et al. https://doi.org/10.3389/frwa.2026.1916396
237. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. https://doi.org/10.5194/hess-26-3079-2022
238. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. https://doi.org/10.1002/hyp.14565
239. Beyond prediction accuracy: using differentiable hybrid models as a tool for hydrological knowledge discovery Y. Hu et al. https://doi.org/10.1016/j.jhydrol.2026.136147
240. Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling D. Anh et al. https://doi.org/10.1007/s11269-022-03393-w
241. Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework L. Nguyen et al. https://doi.org/10.3390/w14030300
242. Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths M. Fathi et al. https://doi.org/10.1038/s41598-025-96577-4
243. Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China Y. Li et al. https://doi.org/10.1007/s11356-023-27351-0
244. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. https://doi.org/10.1016/j.jhydrol.2024.130861
245. A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM B. Zhang et al. https://doi.org/10.32604/jrm.2022.021845
246. A dual-stream LSTM–XGBoost framework for mine water inflow forecasting: a case study of the Longfeng coal mine W. Hao et al. https://doi.org/10.1016/j.jhydrol.2026.135728
247. RETRACTED: The construction of urban cultural and creative industries using deep learning and information management Z. Zhao et al. https://doi.org/10.1016/j.heliyon.2024.e33787
248. Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm W. Jayasinghe et al. https://doi.org/10.1109/ACCESS.2021.3135362
249. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
250. Hourly Regional Rainfall–Runoff Prediction Using Transformer with Water Conservation Constraints G. Jing et al. https://doi.org/10.3390/su18010536