Articles | Volume 27, issue 23
https://doi.org/10.5194/hess-27-4227-2023
© Author(s) 2023. 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-27-4227-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2023
Research article |
| 30 Nov 2023
| 30 Nov 2023
Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks
Roberto Bentivoglio
CORRESPONDING AUTHOR
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Elvin Isufi
Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
Sebastiaan Nicolas Jonkman
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Riccardo Taormina
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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- Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network L. Besseling et al. https://doi.org/10.3390/hydrology11090152
- Generation and selection of training events for surrogate flood inundation models N. Fraehr et al. https://doi.org/10.1016/j.jenvman.2024.123570
- A heterogeneous multi-graph spatio-temporal network for runoff forecasting X. Zhou et al. https://doi.org/10.1016/j.engappai.2026.114967
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- Real-time flood maps forecasting for dam-break scenarios with a transformer-based deep learning model M. Pianforini et al. https://doi.org/10.1016/j.jhydrol.2024.131169
- A lake water level prediction method based on data augmentation and Physics-Informed Neural Networks with imbalanced data L. Lu et al. https://doi.org/10.1016/j.jhydrol.2025.134660
- Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation M. El baida et al. https://doi.org/10.1007/s11269-024-03886-w
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- SAGAM: A Self-Supervised Graph Learning Framework with Supervised Fine-Tuning for Mineral Prospectivity Mapping M. Xie et al. https://doi.org/10.1007/s11053-026-10663-6
- SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation W. Song et al. https://doi.org/10.1016/j.jhydrol.2025.133280
- Coastal storm-induced flooding risk of the New York City subway amid climate change Y. Miura et al. https://doi.org/10.1016/j.trd.2025.104974
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- An interpretable AutoML–SHAP approach for rapid urban pluvial flooding prediction W. Chu et al. https://doi.org/10.1016/j.envsoft.2026.106985
- A method for fast prediction of flood evolution process based on graph neural network X. Shao et al. https://doi.org/10.1016/j.cag.2025.104471
- 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
- U-RNN high-resolution spatiotemporal nowcasting of urban flooding X. Cao et al. https://doi.org/10.1016/j.jhydrol.2025.133117
- Spatio-Temporal Graph Convolutional Network Incorporating Knowledge Graph Embeddings for Hydrological Time Series Prediction X. Jin et al. https://doi.org/10.1109/ACCESS.2026.3656563
- Interpretable physics-informed graph neural networks for flood forecasting M. Taghizadeh et al. https://doi.org/10.1111/mice.13484
- FloodForecaster: A domain-adaptive geometry-informed neural operator framework for rapid flood forecasting M. Taghizadeh et al. https://doi.org/10.1016/j.jhydrol.2025.134512
Saved (final revised paper)
Latest update: 28 May 2026
Short summary
To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.
To overcome the computational cost of numerical models, we propose a deep-learning approach...
