12 citations as recorded by crossref.

1. FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods M. Pianforini et al. https://doi.org/10.1016/j.envsoft.2025.106599
2. Probabilistic flood hazard mapping for dike-breach floods via graph neural networks R. Bentivoglio et al. https://doi.org/10.5194/nhess-26-2089-2026
3. A Physical-Enhanced Spatio-Temporal Graph Convolutional Network for River Flow Prediction R. Huang et al. https://doi.org/10.3390/app15169054
4. Spatiotemporal Graph Learning on Urban Environments H. Li et al. https://doi.org/10.1021/acs.est.5c12640
5. Forecasting of flash flood disasters in small and medium catchments using an improved SVSMR + LSTM model Z. Xu et al. https://doi.org/10.1080/19942060.2026.2678105
6. Multi-scale hydraulic graph neural networks for flood modelling R. Bentivoglio et al. https://doi.org/10.5194/nhess-25-335-2025
7. GTNet: A Graph–Transformer Neural Network for Robust Ecological Health Monitoring in Smart Cities M. Aldossary https://doi.org/10.3390/math14010064
8. Dual-Attention ResUNet With Masked Focal-Tversky Loss for Robust SAR-Based Flood Mapping A. Das et al. https://doi.org/10.1109/ACCESS.2025.3637023
9. An explainable physics-aware deep learning framework with improved spatiotemporal dependence matrices and signal decomposition for multi-station uncertainty daily runoff simulation W. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135625
10. Predicting flood energy reduction in vegetated open channel: Comparative assessment of hybrid artificial intelligence techniques A. Rezzoug et al. https://doi.org/10.1016/j.engappai.2025.111756
11. Convolutional neural network model for rapid prediction of urban flash flood water depth and velocity maps M. Asif et al. https://doi.org/10.1016/j.jhydrol.2026.135601
12. Integrating XGBoost and SHAP to uncover feature contributions for river network selection across different patterns H. Yu et al. https://doi.org/10.1016/j.jag.2026.105120