164 citations as recorded by crossref.

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2. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis Q. Tian et al. 10.3390/w15183184
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9. Evaluation of the hydrological utility of the GPM IMERG satellite precipitation products H. Meng & T. Zhao 10.1016/j.atmosres.2025.108139
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12. 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
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19. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
20. Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui 10.5194/hess-25-5839-2021
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24. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
25. A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique D. Kim et al. 10.3390/w15050972
26. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. 10.3390/w17060907
27. Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al. 10.3390/w15152760
28. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
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30. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia 10.1016/j.jhydrol.2023.130421
31. 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
32. A Runoff Prediction Model Based on Nonhomogeneous Markov Chain W. Li et al. 10.1007/s11269-022-03091-7
33. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
34. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
35. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
36. Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China L. Xing et al. 10.1016/j.compag.2023.108139
37. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al. 10.1016/j.ejrh.2024.102062
38. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. 10.5194/hess-29-1061-2025
39. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
40. 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
41. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
42. A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. 10.1007/s11269-023-03583-0
43. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
44. Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China H. Xue et al. 10.1016/j.ejrh.2024.102100
45. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
46. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
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48. Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China Y. Wang et al. 10.1007/s11356-022-22536-5
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50. A Deep Reinforcement Learning Framework for Cascade Reservoir Operations Under Runoff Uncertainty J. Xu et al. 10.3390/w17152324
51. Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China T. Zhang et al. 10.1016/j.ecolind.2024.112715
52. Mining relevant partial periodic pattern of multi-source time series data Y. Xun et al. 10.1016/j.ins.2022.10.049
53. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
54. Causality-Structured Deep Learning for Soil Moisture Predictions L. Li et al. 10.1175/JHM-D-21-0206.1
55. Advancements in rainfall-runoff prediction: Exploring state-of-the-art neural computing modeling approaches D. Irwan et al. 10.1016/j.aej.2025.02.060
56. Hydrology-informed machine learning for enhancing surface runoff elucidation P. Hua et al. 10.1016/j.scitotenv.2025.179936
57. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
58. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
59. Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study H. Yang & W. Li 10.1007/s11269-022-03389-6
60. Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm S. Liao et al. 10.1007/s11269-023-03442-y
61. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
62. 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. 10.3390/w16213133
63. 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
64. 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
65. 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 10.1016/j.agwat.2024.109178
66. 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
67. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
68. The Social Interaction of Online Products in E-Commerce Live Broadcast under Generative Artificial Intelligence W. Sun et al. 10.4018/IJITSA.386162
69. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
70. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
71. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al. 10.1038/s41597-025-05612-6
72. Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation J. Ougahi & J. Rowan 10.1038/s41598-025-87187-1
73. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al. 10.3390/rs13122272
74. 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
75. 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. 10.3390/w16081179
76. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. 10.1016/j.ejrh.2024.101776
77. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. 10.1016/j.jhydrol.2023.129990
78. Earth and Planetary Surface Processes Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science C. Burberry et al. 10.1029/2022EA002414
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80. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
81. 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
82. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
83. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al. 10.1007/s11269-023-03552-7
84. 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. 10.1061/JHYEFF.HEENG-6395
85. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
86. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al. 10.1016/j.jhydrol.2022.127431
87. Japanese waka translation supported by internet of things and artificial intelligence technology R. Shen 10.1038/s41598-025-85184-y
88. 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
89. 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
90. Simulating the climate driven runoff in data-scarce mountains by machine learning and downscaling reanalysis data M. Fan et al. 10.1007/s00477-022-02231-0
91. Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation L. Lang et al. 10.1016/j.jhydrol.2025.132806
92. 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
93. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework L. Zhang et al. 10.3390/atmos15020164
94. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
95. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
96. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
97. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al. 10.3390/w16192805
98. 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
99. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
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102. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
103. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
104. Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting Y. Tian et al. 10.1007/s12145-025-01907-9
105. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
106. Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach R. Gupta et al. 10.1016/j.compag.2024.109355
107. Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning J. Choi et al. 10.4491/KSEE.2024.46.10.602
108. Study on the Optimization and Stability of Machine Learning Runoff Prediction Models in the Karst Area C. Mo et al. 10.3390/app12104979
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110. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. 10.3390/w14111745
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112. Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation W. Li et al. 10.1038/s41598-024-62127-7
113. Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes R. Ahsan et al. 10.1016/j.imu.2022.100860
114. 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. 10.3390/w17050750
115. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
116. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
117. Lowland transboundary river in a cold, semi-arid steppe: review of the Yesil River basin N. Ongdas et al. 10.1007/s12665-025-12500-0
118. 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
119. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. 10.1016/j.jhydrol.2022.128150
120. Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches V. Kartal et al. 10.1016/j.pce.2024.103757
121. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
122. Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting A. Amini et al. 10.1029/2022WR032789
123. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
124. 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. 10.1016/j.scitotenv.2023.165494
125. Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model S. Gegenleithner et al. 10.5194/hess-29-1939-2025
126. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
127. Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand P. Panyadee & P. Champrasert 10.3390/su16114433
128. Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models S. Wang et al. 10.1007/s11269-022-03305-y
129. Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data L. Wang et al. 10.3390/app132011151
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131. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
132. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al. 10.1016/j.jhydrol.2025.133689
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136. Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media X. Zeng & Z. Zhong 10.4018/JOEUC.314786
137. An efficient parallel runoff forecasting model for capturing global and local feature information Y. Hong et al. 10.1038/s41598-025-96940-5
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143. Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework L. Nguyen et al. 10.3390/w14030300
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147. Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China Y. Li et al. 10.1007/s11356-023-27351-0
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151. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
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153. The construction of urban cultural and creative industries using deep learning and information management Z. Zhao et al. 10.1016/j.heliyon.2024.e33787
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156. Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models A. Amini et al. 10.1016/j.jhydrol.2024.130804
157. 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
158. 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
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161. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
162. Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins T. Bajirao et al. 10.1007/s00704-021-03681-2
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