31 citations as recorded by crossref.

1. Enhancing streamflow predictions through basin-to-basin knowledge transfer: A novel strategy for deep learning models adaptation and generalization K. Nifa et al. https://doi.org/10.1016/j.rineng.2025.107978
2. Spatio-Temporal Attention in Federated Learning for Streamflow Forecasting A. Ashiru et al. https://doi.org/10.1007/s11269-026-04671-7
3. Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations B. Yu et al. https://doi.org/10.1016/j.jhydrol.2025.133052
4. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al. https://doi.org/10.5194/hess-30-757-2026
5. Explainable Monitoring Model Based on AE-BiGRU and SHAP Analysis of Seepage Pressure for Concrete Dams J. Xie et al. https://doi.org/10.3390/w18050614
6. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al. https://doi.org/10.1016/j.envsoft.2025.106691
7. 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
8. Multi-step reservoir inflow prediction using a rolling window strategy and decomposed LSTM W. Thaisiam et al. https://doi.org/10.1016/j.wse.2025.11.001
9. Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. https://doi.org/10.1016/j.jhydrol.2024.132165
10. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
11. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al. https://doi.org/10.1016/j.ejrh.2025.102998
12. Simulación del caudal en España utilizando redes neuronales Long Short-Term Memory J. Casado-Rodríguez et al. https://doi.org/10.4995/ia.25084
13. 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
14. The comparison of analytical hierarchy process, frequency ratio, and deep learning-based approaches for landslide susceptibility mapping in the Northern Chiang Mai watershed Basins, Thailand P. Manopkawee et al. https://doi.org/10.1186/s40677-025-00353-5
15. 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
16. Deep recurrent neural networks for water hammer transient prediction and dynamic protection optimization in long distance pipelines R. Dong et al. https://doi.org/10.1038/s41598-026-41915-3
17. 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
18. Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations S. Mun & J. Yoo https://doi.org/10.3390/electronics13224413
19. Trends and Transitions in Hydrological Modelling: A Systematic Review of Model Approaches and Applications (2015–2025) T. Peerbhai et al. https://doi.org/10.1007/s11269-026-04815-9
20. Wire rope self-rotation measurement method based on sliding column block modeling and one-dimensional convolutional neural network K. Jiang et al. https://doi.org/10.1088/1361-6501/ae02bb
21. Neural Network-based Performance Prediction of Heat Pipe and Loop Heat Pipe Heat Exchangers: A State-of the- Art Review E. Amruth et al. https://doi.org/10.18311/jmmf/2025/49662
22. Incorporating hydrological constraints with deep learning for streamflow prediction Y. Zhou et al. https://doi.org/10.1016/j.eswa.2024.125379
23. Modelling and Optimisation of Hysteresis and Sensitivity of Multicomponent Flexible Sensing Materials K. Chen et al. https://doi.org/10.3390/app15063271
24. 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
25. 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
26. Streamflow prediction in the Danube River Basin using a multi-source graph-integrated GCN-LSTM model M. Sun et al. https://doi.org/10.1016/j.ejrh.2026.103275
27. Integrating machine learning with a novel karst hydrological model to enhance extreme streamflow simulation in karst regions Q. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135572
28. Privacy-Preserving Federated Learning for Hydrological Forecasting in the Chu–Talas Basin R. Amanzholova et al. https://doi.org/10.3390/w18111361
29. Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin T. Doan Quang et al. https://doi.org/10.2478/cee-2026-0042
30. Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization H. Kilinc et al. https://doi.org/10.1007/s11600-025-01570-0
31. Comparative assessment of hydrological and deep learning models for runoff simulation and water storage in irrigated basins A. Razeghi Haghighi et al. https://doi.org/10.1007/s40808-025-02665-9