114 citations as recorded by crossref.

1. Flood Impact Assessment, LSTM-Based Forecasting, and Revenue Loss Analysis of the Melamchi Water Supply Project K. Katwal et al. https://doi.org/10.11648/j.ijdsa.20251105.11
2. Predicting Délı̨nę ice road surface temperatures on Great Bear Lake in the Northwest Territories, Canada using long short-term memory networks and their relationship to ice melt processes I. Turnbull et al. https://doi.org/10.1016/j.coldregions.2026.104963
3. Climate teleconnection-driven stochastic simulation for future water-related risk management T. Lee & T. Ouarda https://doi.org/10.1016/j.jhydrol.2025.133834
4. A GNN routing module is all you need for LSTM Rainfall–Runoff models H. Mosaffa et al. https://doi.org/10.5194/hess-30-2079-2026
5. Utilizing Deep Learning Models to Predict Streamflow H. Workneh & M. Jha https://doi.org/10.3390/w17050756
6. 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
7. Enhancing daily streamflow prediction: A comparative analysis of univariate LSTM and N-BEATS models with coupled SWAT-LSTM and SWAT-N-BEATS models incorporating influential SWAT features R. Priya & R. Manjula https://doi.org/10.1007/s12040-025-02567-5
8. Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions Z. Yi et al. https://doi.org/10.1016/j.ejrh.2026.103116
9. 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
10. A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting A. Abdoulhalik & A. Ahmed https://doi.org/10.3390/su16104005
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12. Forecasting the frequency and magnitude of hurricanes in the Yucatan Peninsula, Mexico, in the period from 2025 to 2034 using convolutional neural networks (CNNs), Long Short-Term Memory networks (LSTMs) and statistical models H. De Gracia et al. https://doi.org/10.1016/j.tcrr.2025.07.006
13. Deep Learning-Based Correction of Decadal Predictions of the PDO and TAG Indices J. Dixit et al. https://doi.org/10.1109/LGRS.2026.3662257
14. Deep learning-constrained projection of global fluvial floods and their socioeconomic implications under global warming X. Huang et al. https://doi.org/10.1088/1748-9326/ae19fe
15. Enhancing flood prediction in the Lower Mekong River Basin by a scale-independent interpretable deep learning model Y. Qiu et al. https://doi.org/10.1016/j.eiar.2025.108130
16. Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis K. Merabet et al. https://doi.org/10.1038/s41598-026-38757-4
17. Identifying trustworthiness challenges in deep learning models for continental-scale water quality prediction X. Xia et al. https://doi.org/10.1016/j.ynexs.2025.100104
18. Improving transparency in karst spring discharge and water quality forecasts using interpretable machine learning models in the Eastern Alps A. Pölz et al. https://doi.org/10.1016/j.ejrh.2026.103147
19. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. https://doi.org/10.1016/j.ejrh.2024.101744
20. 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
21. Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani https://doi.org/10.1016/j.advwatres.2025.105063
22. Improving Daily Streamflow Predictions over Large Watersheds: Introducing a Novel Enhanced Long Short-Term Memory (En-LSTM) Model P. Sharma et al. https://doi.org/10.1007/s11269-025-04475-1
23. A hybrid SWAT-LSTM model for streamflow simulation with SHAP-based interpretability: Application in the Wei River Basin, China W. Zhou et al. https://doi.org/10.1016/j.ejrh.2026.103254
24. Application of LSTM coupled models in runoff simulation and prediction: a review S. Li et al. https://doi.org/10.1007/s13201-025-02747-0
25. FEATURE SELECTION METHODS FOR LSTM-BASED RIVER WATER LEVEL AND DISCHARGE FORECASTING A. Alzhanov & A. Nugumanova https://doi.org/10.37943/21EHLH9882
26. A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series H. Dastour & Q. Hassan https://doi.org/10.3390/hydrology10040095
27. 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
28. Spatial downscaling of GRACE terrestrial water storage anomalies for drought and flood potential assessment G. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.133144
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31. Applications of machine learning to water resources management: A review of present status and future opportunities A. Ahmed et al. https://doi.org/10.1016/j.jclepro.2024.140715
32. Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment A. Geetha Raveendran Nair et al. https://doi.org/10.3390/su151914148
33. Combining Standard Artificial Intelligence Models, Pre-Processing Techniques, and Post-Processing Methods to Improve the Accuracy of Monthly Runoff Predictions in Karst-Area Watersheds C. Mo et al. https://doi.org/10.3390/app13010088
34. Deep Learning Ensemble for Flood Probability Analysis F. Sseguya & K. Jun https://doi.org/10.3390/w16213092
35. Enhanced Time Series–Physics Model Approach for Dam Discharge Impacts on River Levels: Seomjin River, South Korea C. Jung et al. https://doi.org/10.3390/w17213057
36. The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. https://doi.org/10.5194/hess-27-501-2023
37. Non-Stationarity of Hydroclimatic Memory—Is Hydrological Memory Changing Under Climate Warming? M. Birylo https://doi.org/10.3390/w18070869
38. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al. https://doi.org/10.3389/frwa.2025.1595898
39. Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS C. Prudhomme et al. https://doi.org/10.1002/met.2192
40. Streamflow simulation at different temporal scales under rating curve uncertainty conditions using machine learning models N. Mena et al. https://doi.org/10.2166/nh.2024.080
41. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al. https://doi.org/10.1080/02626667.2025.2593333
42. Bias correction of Global Flood Awareness System (GloFAS) data for multi-station river flow prediction by ensemble modelling V. Nourani et al. https://doi.org/10.1080/19942060.2026.2665857
43. Decoding LSTM to Reveal Baseflow Contributions in Fractured and Sedimentary Mountain Basins: A Case Study in the Sangre de Cristo Mountains, Southwestern United States M. Rosati et al. https://doi.org/10.3390/hydrology13020051
44. Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers G. Jesus et al. https://doi.org/10.3390/s25072154
45. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. https://doi.org/10.5194/hess-28-2871-2024
46. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. https://doi.org/10.3390/w16152161
47. Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al. https://doi.org/10.3390/w15030427
48. Integrated probabilistic forecasting framework for long-term reservoir outflow through dynamic coupling of meteorological–hydrological–engineering processes J. Tan et al. https://doi.org/10.1016/j.jhydrol.2025.134214
49. Detecting sun glint in UAV RGB images at different times using a deep learning algorithm J. Chen et al. https://doi.org/10.1016/j.ecoinf.2024.102660
50. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani https://doi.org/10.1038/s41597-025-04389-y
51. Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana J. Katsekpor et al. https://doi.org/10.1002/met.2226
52. Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana J. Katsekpor et al. https://doi.org/10.1016/j.envc.2025.101181
53. Impact of climate change on hydrological processes in data-scarce regions: A case study in Tianshan Mountain, Central Asia H. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103553
54. Integrating process-based and deep learning models for flood simulation in karst basins B. Li et al. https://doi.org/10.1016/j.wse.2025.11.005
55. Daily river flow simulation using ensemble disjoint aggregating M5-Prime model K. Khosravi et al. https://doi.org/10.1016/j.heliyon.2024.e37965
56. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. https://doi.org/10.1016/j.advwatres.2023.104569
57. Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al. https://doi.org/10.1007/s11269-025-04332-1
58. Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco K. Nifa et al. https://doi.org/10.3390/w15020262
59. Optimizing runoff simulation in three mid-high latitude catchments by integrating terrestrial ecosystem modelling, hybrid machine learning, and causal inference H. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103085
60. A Novel Spectral Decomposition Framework for TimesNet: Integrating DCT and Wavelet Transforms for Hydrological Forecasting K. Küllahcı et al. https://doi.org/10.1007/s11269-026-04529-y
61. KI-Modellierung für das Quellmanagement: Vorhersage von Karstquellschüttung und Wasserqualität mittels interpretierbarer Machine-Learning-Modelle A. Pölz et al. https://doi.org/10.1007/s00506-026-01221-8
62. Fusion of data-driven models with a knowledge-guided loss function for flood forecasting H. Malik et al. https://doi.org/10.1016/j.asoc.2025.113742
63. Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs R. Qiu et al. https://doi.org/10.1016/j.watres.2024.121314
64. Reservoir computing for modeling and predicting stream chemistry P. Allsup et al. https://doi.org/10.2166/hydro.2025.213
65. 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
66. Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data A. Karamvand et al. https://doi.org/10.1016/j.pce.2024.103737
67. Quantifying the Effects of National Water Model Freshwater Flux Predictions on Estuarine Hydrodynamic Forecasts N. Chin et al. https://doi.org/10.1111/1752-1688.70033
68. Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area C. Mo et al. https://doi.org/10.3390/app13095693
69. Machine learning characterization of fuel temperature and moisture dynamics in wildland-urban interface for urban fire management J. Liu et al. https://doi.org/10.1016/j.jenvman.2026.129717
70. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish https://doi.org/10.2166/wst.2023.330
71. Enhancing hydrological hazard early warning: a 60 d streamflow forecasting framework integrating deep learning and process-based modeling Z. Liu et al. https://doi.org/10.5194/nhess-26-2353-2026
72. Hybrid neutral network for daily streamflow projection on a sub-seasonal timescale E. Castro et al. https://doi.org/10.5327/Z2176-94782495
73. Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al. https://doi.org/10.1016/j.jhydrol.2025.134711
74. Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. https://doi.org/10.3390/jmse11112154
75. Factors driving user behavior and value creation with text-to-image generative artificial intelligence (AI): A systems theory perspective C. Hung et al. https://doi.org/10.1016/j.techsoc.2025.103005
76. A lake water level prediction method based on data augmentation and Physics-Informed Neural Networks with imbalanced data L. Lu et al. https://doi.org/10.1016/j.jhydrol.2025.134660
77. Explainable Machine Learning for Streamflow Forecasting: Application to the Bosna River Basin S. Gnjato et al. https://doi.org/10.3390/w18101226
78. Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al. https://doi.org/10.1007/s11431-023-2757-2
79. Hydro–Meteorological Coupled Runoff Forecasting Using Multi-Model Precipitation Forecasts Z. Zhu et al. https://doi.org/10.3390/w18050638
80. 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. https://doi.org/10.3390/w16040593
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83. Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight F. Granata & F. Di Nunno https://doi.org/10.1016/j.jhydrol.2023.129888
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