32 citations as recorded by crossref.

1. Strategies for Implementing Deep Learning Techniques for Rainfall-Runoff Modeling in a River Having Sparse Data D. Chandran & N. Chithra https://doi.org/10.1007/s11269-026-04585-4
2. Investigation of the effect of climatic parameters in machine learning algorithms for streamflow predicting in Hamoon Helmand Catchment, Iran S. Vakili & S. Mousavi https://doi.org/10.1007/s12517-025-12243-z
3. A Novel Evidential Uncertainty Framework for Hybrid Models in Rainfall Simulation H. Ebrahimi https://doi.org/10.1007/s11269-025-04386-1
4. 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
5. Enhancing Daily Runoff Prediction Accuracy with the Coupled KAN-LSTM Model and a Dynamic Postprocessing Module C. Mo et al. https://doi.org/10.1007/s11269-026-04787-w
6. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al. https://doi.org/10.1016/j.jhydrol.2025.134890
7. Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al. https://doi.org/10.1109/TGRS.2025.3605332
8. A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133683
9. 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
10. REAL-TIME FLOOD RUNOFF PREDICTION FOR A SMALL-TO-MEDIUM-SCALE URBAN RIVER WATERSHED USING A BIDIRECTIONAL LONG-SHORT-TERM MEMORY MODEL (BiLSTM) C. SUBRAMANIYAM et al. https://doi.org/10.2208/journalofjsce.24-27025
11. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. https://doi.org/10.1016/j.ejrh.2025.102426
12. Hybridizing Machine and Deep Learning for Urban Water Demand Forecasting: An Ensemble Framework Leveraging Dam Monitoring Data M. Akiner https://doi.org/10.1007/s00024-026-03941-0
13. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
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16. Daily Runoff Prediction Method Based on Secondary Decomposition and the GTO-Informer-GRU Model H. Yu et al. https://doi.org/10.3390/w17182775
17. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés https://doi.org/10.1016/j.jhydrol.2024.132569
18. A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models S. Yoon et al. https://doi.org/10.1016/j.jhydrol.2026.135097
19. Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106374
20. Bottom-up assessment of climate change vulnerability of a large and complex river basin using emulator models A. John et al. https://doi.org/10.1016/j.ejrh.2025.103095
21. Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. https://doi.org/10.5194/hess-29-5453-2025
22. 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
23. A novel data cleaning and completion framework for NB-IoT water meters in district metered areas based on purification ratio coefficients J. Zhang et al. https://doi.org/10.2166/aqua.2025.035
24. Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia N. Bekele Mena et al. https://doi.org/10.2166/wcc.2024.645
25. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. https://doi.org/10.3390/w16192749
26. Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition Y. Jia et al. https://doi.org/10.1007/s11269-025-04447-5
27. A hybrid ensemble deep learning model for advanced time series rainfall forecasting using satellite data and climate variability analysis S. Hasan et al. https://doi.org/10.1016/j.aiig.2026.100217
28. Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy H. Sanikhani et al. https://doi.org/10.1007/s11269-025-04106-9
29. Unraveling the impacts of GCM input averaging and hydrological model output averaging on runoff projections under climate change Y. Hu et al. https://doi.org/10.1016/j.jenvman.2026.129911
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