22 citations as recorded by crossref.

1. An explainable deep learning approach for hydropower production forecasting: Evidence from 116 plants in China P. Du et al. https://doi.org/10.1016/j.energy.2026.141237
2. Intelligent daily rainfall prediction for early warning using deep learning and satellite data: application to Bouaflé and Zuénoula stations, Ivory coast S. Kamenan et al. https://doi.org/10.5194/ascmo-12-173-2026
3. Comparison of LSTM and conceptual hydrological models for runoff simulation under changing climatic conditions in Iran A. Jahanshahi & M. Booij https://doi.org/10.1016/j.jaridenv.2026.105679
4. Towards more accurate very short-range flood forecasts with physics-enhanced machine learning models R. Bezerra et al. https://doi.org/10.2166/hydro.2026.180
5. Geospatial and Deep Learning Approaches for Modeling Floodwater Depth in Urbanized Areas J. Blay & L. Hashemi-Beni https://doi.org/10.3390/rs18010060
6. A Hybrid Rank-Preserving and Evolutionary Algorithm for Multisite Daily Streamflow Simulation S. Pitulić et al. https://doi.org/10.3390/a19070541
7. 3D finite-volume modeling of saltwater intrusion dynamics and mitigation under drought conditions: A case study of the Minjiang river estuary, China Y. Huang et al. https://doi.org/10.1016/j.ecss.2026.110025
8. 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
9. Hybrid singular spectrum analysis and LSTM modeling for daily precipitation forecasting Z. Lu et al. https://doi.org/10.1016/j.geog.2025.12.007
10. 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
11. Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
12. Physics-Informed Data-Driven Models for Streamflow Prediction in Small Catchments: Combining Hydrological Causality and Machine Learning Frameworks V. Galán et al. https://doi.org/10.3390/su18136381
13. Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
14. Benchmark-guided insights into hybrid process-based-deep learning models for continental-scale streamflow prediction across the United States D. Sourya et al. https://doi.org/10.1016/j.ejrh.2026.103636
15. Bridging gaps in sparse groundwater data: classical, ensemble, and deep learning approaches for single- and multi-well imputation H. Sejine et al. https://doi.org/10.3389/frwa.2025.1726853
16. Reconstruction of Daily Runoff Series in Data-Scarce Areas Based on Physically Enhanced Seq-to-Seq-Attention-LSTM Model Z. Yin et al. https://doi.org/10.3390/w17233396
17. A transfer learning approach to improve suspended sediment concentration prediction in data-scarce rivers T. Hamadene et al. https://doi.org/10.1007/s11368-026-04375-w
18. Phased dynamic analysis and prediction of rice Chilo suppressalis integrating remote sensing physiological indices and environmental factors Y. Wu et al. https://doi.org/10.1016/j.jia.2026.01.043
19. The evaluation, attribution and ecological implications of downstream flow regime changes in a large transboundary basin in Central Asia L. Xu et al. https://doi.org/10.1016/j.ejrh.2026.103665
20. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
21. A classified artificial intelligence model based on triple-attention mechanism and metaheuristic algorithm for monthly runoff prediction W. Liu et al. https://doi.org/10.1016/j.ejrh.2026.103441
22. Enhancing the performance of machine learning models for simulation of river flows in data-scarce catchments using bootstrapping and physics-based data augmentation J. Murungi et al. https://doi.org/10.1016/j.engappai.2026.115603