Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-335-2025
https://doi.org/10.5194/nhess-25-335-2025
Research article
 | 
23 Jan 2025
Research article |  | 23 Jan 2025

Multi-scale hydraulic graph neural networks for flood modelling

Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina

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Manuscript not accepted for further review
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Cited articles

Bentivoglio, R.: Raw datasets for paper “Multi-scale hydraulic graph neural networks for flood modelling”, Zenodo [data set], https://doi.org/10.5281/zenodo.13326595, 2024. a
Bentivoglio, R.: Code Repository for paper ”Multi-scale hydraulic graph neural networks for flood modelling”, GitHub [code], https://github.com/RBTV1/mSWE-GNN, last access: 28 November 2024. a
Bentivoglio, R.: RBTV1/mSWE-GNN: Published version (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.14673842, 2025. a
Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Deep learning methods for flood mapping: a review of existing applications and future research directions, Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, 2022. a
Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks, Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l
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Short summary
Deep learning methods are increasingly used as surrogates for spatio-temporal flood models but struggle with generalization and speed. Here, we propose a multi-resolution approach using graph neural networks that predicts dike breach floods across different meshes, topographies, and boundary conditions with high accuracy and up to 1000× speed-ups. The model also generalizes to larger more complex case studies with just one additional simulation for fine-tuning.
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