Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5443-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5443-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2024
Research article |
| 18 Dec 2024
| 18 Dec 2024
Enhancing generalizability of data-driven urban flood models by incorporating contextual information
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Milton Salvador Gomez
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland
Tom Beucler
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland
Jovan Blagojevic
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
João Paulo Leitao
Department of Urban Water Management, Swiss Federal Institute of Aquatic Science and Technology, Dubendorf, Switzerland
Nadav Peleg
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland
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Cited
16 citations as recorded by crossref.
- How can hydrological connectivity inform catchment scale stormwater flood management? Y. Yao et al. https://doi.org/10.1016/j.watres.2026.125767
- Multi-scale hydraulic graph neural networks for flood modelling R. Bentivoglio et al. https://doi.org/10.5194/nhess-25-335-2025
- 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
- Unraveling nonlinear urban waterlogging responses to rainfall structure: A data-driven analysis in a highly urbanized megacity Z. Zhou et al. https://doi.org/10.1016/j.jhydrol.2025.134349
- 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
- Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling W. Song et al. https://doi.org/10.1080/19942060.2025.2481115
- Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al. https://doi.org/10.1016/j.jhydrol.2026.135350
- Bayesian-optimized BiLSTM-U-Net framework for urban flood prediction with spatio-temporal feature integration X. Yao et al. https://doi.org/10.1016/j.jhydrol.2026.135175
- Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al. https://doi.org/10.1016/j.ecoinf.2025.103526
- Exploring big data applications in sustainable urban infrastructure: A review D. Ogunkan & S. Ogunkan https://doi.org/10.1016/j.ugj.2025.02.003
- 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
- Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India A. Arora et al. https://doi.org/10.1038/s41598-025-07403-w
- Simulating hurricane-induced compound flooding via spatiotemporal analysis of satellite-derived inundation maps A. Rostami et al. https://doi.org/10.1016/j.rsase.2025.101771
- Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification W. Gao et al. https://doi.org/10.1016/j.jhydrol.2026.135119
- Blue-Green Infrastructure for pluvial flood risk reduction in rapidly urbanizing peri-urban areas: Strategic planning for uncertain futures F. Fappiano et al. https://doi.org/10.1016/j.jenvman.2025.127843
- Ozone Trends and Mortality Risk: The Growing Need for Machine Learning Predictions in Bogotá, Colombia D. Bustos et al. https://doi.org/10.1007/s41748-026-01052-3
16 citations as recorded by crossref.
- How can hydrological connectivity inform catchment scale stormwater flood management? Y. Yao et al. https://doi.org/10.1016/j.watres.2026.125767
- Multi-scale hydraulic graph neural networks for flood modelling R. Bentivoglio et al. https://doi.org/10.5194/nhess-25-335-2025
- 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
- Unraveling nonlinear urban waterlogging responses to rainfall structure: A data-driven analysis in a highly urbanized megacity Z. Zhou et al. https://doi.org/10.1016/j.jhydrol.2025.134349
- 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
- Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling W. Song et al. https://doi.org/10.1080/19942060.2025.2481115
- Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al. https://doi.org/10.1016/j.jhydrol.2026.135350
- Bayesian-optimized BiLSTM-U-Net framework for urban flood prediction with spatio-temporal feature integration X. Yao et al. https://doi.org/10.1016/j.jhydrol.2026.135175
- Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al. https://doi.org/10.1016/j.ecoinf.2025.103526
- Exploring big data applications in sustainable urban infrastructure: A review D. Ogunkan & S. Ogunkan https://doi.org/10.1016/j.ugj.2025.02.003
- 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
- Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India A. Arora et al. https://doi.org/10.1038/s41598-025-07403-w
- Simulating hurricane-induced compound flooding via spatiotemporal analysis of satellite-derived inundation maps A. Rostami et al. https://doi.org/10.1016/j.rsase.2025.101771
- Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification W. Gao et al. https://doi.org/10.1016/j.jhydrol.2026.135119
- Blue-Green Infrastructure for pluvial flood risk reduction in rapidly urbanizing peri-urban areas: Strategic planning for uncertain futures F. Fappiano et al. https://doi.org/10.1016/j.jenvman.2025.127843
- Ozone Trends and Mortality Risk: The Growing Need for Machine Learning Predictions in Bogotá, Colombia D. Bustos et al. https://doi.org/10.1007/s41748-026-01052-3
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
We introduce a new deep-learning model that addresses the limitations of existing urban flood models in handling varied terrains and rainfall events. Our model subdivides a city into small patches and presents a novel approach to incorporate broader terrain information. It accurately predicts high-resolution flood maps across diverse rainfall events and cities (on minute and meter scales) that haven’t been seen by the model, which offers valuable insights for urban flood mitigation strategies.
We introduce a new deep-learning model that addresses the limitations of existing urban flood...
