37 citations as recorded by crossref.

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4. Integrating Explicit Dam Release Prediction into Fluvial Forecasting Systems J. Pinho & W. Weber de Melo https://doi.org/10.3390/su172310671
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9. Synergizing machine learning and hydrological model to enhance water availability and demand forecasting in Godawari, Nepal M. Agaasti et al. https://doi.org/10.1016/j.gsd.2026.101592
10. The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment A. Tarpanelli et al. https://doi.org/10.1007/s10712-026-09935-w
11. Earthquake correction enabled holistic assessment of water storage variations in Japan . Abhishek et al. https://doi.org/10.1007/s10040-026-03083-6
12. Emulation of monthly water allocations using LSTM models: a case study of the Colorado River Basin in Texas K. Bonney et al. https://doi.org/10.1088/2515-7620/ae4cf9
13. An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM F. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106524
14. Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions Y. He & X. Yang https://doi.org/10.5194/gmd-18-9257-2025
15. Climate-adaptive and low-carbon multi-objective water resources optimization in arid oasis cities H. Shao et al. https://doi.org/10.1016/j.isci.2026.116081
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17. Streamflow Forecasting: A Comparative Analysis of ARIMAX, Rolling Forecasting LSTM Neural Network and Physically Based Models in a Pristine Catchment D. Perazzolo et al. https://doi.org/10.3390/w17152341
18. Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
19. A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106802
20. Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. https://doi.org/10.5194/hess-29-5099-2025
21. Deep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation R. Vazirian https://doi.org/10.1061/JHYEFF.HEENG-6651
22. A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. https://doi.org/10.1016/j.jhydrol.2025.134082
23. Multi-model integration framework for monthly runoff prediction based on variational mode decomposition (VMD) and trend-based modeling S. Wang et al. https://doi.org/10.1007/s00477-026-03170-w
24. An explainable deep learning model based on hydrological principles for flood simulation and forecasting X. Xiang et al. https://doi.org/10.5194/hess-29-7217-2025
25. Agentic SWMM: Auditable and Reproducible Stormwater Modelling Workflow with Agent Skills and Model Context Protocol Z. Zhang & C. Valeo https://doi.org/10.3390/aieng1010005
26. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. https://doi.org/10.1016/j.jhydrol.2025.134270
27. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
28. Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv https://doi.org/10.1016/j.jhydrol.2025.133033
29. Overcoming hydrological forecasting challenges through augmented adaptive deep algorithms: a case study of the great lakes across Canada and the U.S M. Zeynoddin et al. https://doi.org/10.1007/s00704-025-05819-y
30. A New Method for Detecting Automated Mapping Anomalies in Himalayan Glacial Lakes from Satellite Images X. Jiang et al. https://doi.org/10.3390/rs18010061
31. 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
32. A comparative analysis of machine learning models for predicting groundwater and surface water in a stressed semi-arid watershed: The Khanmirza case study Z. Ebrahimzadeh et al. https://doi.org/10.1007/s12665-025-12801-4
33. 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
34. 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
35. Mind the gap: A hydrogeological forecasting-oriented digital twin framework for groundwater levels under sparse irregular sampling C. Silva & R. Manzione https://doi.org/10.1016/j.gsd.2026.101672
36. Case Study on Improvement Measures for Increasing Accuracy of AI-Based River Water-Level Prediction Model S. Kim et al. https://doi.org/10.3390/earth6040146
37. Estimación de caudales en una cuenca tropical mediante modelación neuronal artificial basada en memoria hidrológica L. Gonzalez Salcedo & L. Blandón-López https://doi.org/10.25100/iyc.v28i1.15199