34 citations as recorded by crossref.

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4. Geospatial and Deep Learning Approaches for Modeling Floodwater Depth in Urbanized Areas J. Blay & L. Hashemi-Beni https://doi.org/10.3390/rs18010060
5. Feasibility-guided evolutionary optimization of pump station design and operation in water networks T. Faúndez-Lizama et al. https://doi.org/10.1038/s41598-025-17630-w
6. An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net J. Qiu et al. https://doi.org/10.1016/j.watres.2026.125504
7. Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments W. Song & M. Guan https://doi.org/10.1016/j.watres.2026.125799
8. A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM W. Lou et al. https://doi.org/10.5194/hess-30-1625-2026
9. Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models M. Soliman et al. https://doi.org/10.3390/hydrology12090223
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12. An Open-Source Urban Digital Twin for Enhancing Outdoor Thermal Comfort in the City of Huelva (Spain) V. Lopez-Cabeza et al. https://doi.org/10.3390/smartcities8050160
13. Comparing strategies for training LSTM models for street-scale urban flood prediction in Norfolk, Virginia J. Jeong et al. https://doi.org/10.1016/j.ejrh.2026.103405
14. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. https://doi.org/10.3390/w15091760
15. A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks M. Rehan et al. https://doi.org/10.33093/jiwe.2025.4.2.9
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17. Physics-Guided Deep Learning for Spatiotemporal Evolution of Urban Pluvial Flooding H. Woo et al. https://doi.org/10.3390/w17081239
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19. A hybrid statistical–dynamical framework for compound coastal flooding analysis Z. Wang et al. https://doi.org/10.1088/1748-9326/ad96ce
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21. A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
22. Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler https://doi.org/10.1016/j.hydroa.2023.100167
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24. Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. https://doi.org/10.3390/hydrology10070141
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27. A Study on Flood Susceptibility Mapping in the Poyang Lake Basin Based on Machine Learning Model Comparison and SHapley Additive exPlanations Interpretation Z. Li et al. https://doi.org/10.3390/w17202955
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29. Real-time probabilistic spatiotemporal forecasting of building functionality in flood emergencies: a deep learning approach to facilitate community evacuation planning L. Xie et al. https://doi.org/10.1080/15732479.2025.2529469
30. Machine Learning for Flood Resiliency—Current Status and Unexplored Directions V. Uddameri & E. Hernandez https://doi.org/10.3390/environments12080259
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32. Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms N. Bowsher et al. https://doi.org/10.2166/hydro.2023.123
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34. Brief communication: What do we need to know? Ten questions about climate and water challenges in Berlin-Brandenburg P. Alencar et al. https://doi.org/10.5194/nhess-25-4043-2025