41 citations as recorded by crossref.

1. Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia N. Andika et al. https://doi.org/10.1016/j.rines.2025.100090
2. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
3. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. https://doi.org/10.3390/w17030339
4. Integrating Explicit Dam Release Prediction into Fluvial Forecasting Systems J. Pinho & W. Weber de Melo https://doi.org/10.3390/su172310671
5. Improving Short-Term Runoff Forecasting in a Karst Basin: Integrating ECMWF Precipitation Data with Process-Driven and Machine Learning Models C. Mo et al. https://doi.org/10.1061/JWRMD5.WRENG-7200
6. Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106648
7. Rethinking pathways of ecological management: Insights from climate-driven ecosystem health N. Mutalifu et al. https://doi.org/10.1016/j.ecolind.2026.115512
8. How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. https://doi.org/10.5194/hess-29-5005-2025
9. Integrated hydrological modelling and streamflow characterization of Gangotri Glacier meltwater M. Arora et al. https://doi.org/10.1007/s13201-024-02283-3
10. 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
11. 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
12. Earthquake correction enabled holistic assessment of water storage variations in Japan . Abhishek et al. https://doi.org/10.1007/s10040-026-03083-6
13. 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
14. 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
15. 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
16. 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
17. Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study K. Islam et al. https://doi.org/10.1371/journal.pwat.0000359
18. 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
19. 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
20. 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
21. 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
22. Deep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation R. Vazirian https://doi.org/10.1061/JHYEFF.HEENG-6651
23. Hydrologically constrained genetic programming for interpretable rainfall–runoff model discovery: A process–informed machine learning approach N. Matin et al. https://doi.org/10.1016/j.jhydrol.2026.136493
24. 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
25. 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
26. Streamflow Estimation at Ungauged Sections in a Nested Basin Using an HEG-LSTM Model Y. He et al. https://doi.org/10.3390/w18172145
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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