Articles | Volume 27, issue 23
https://doi.org/10.5194/hess-27-4227-2023
https://doi.org/10.5194/hess-27-4227-2023
Research article
 | 
30 Nov 2023
Research article |  | 30 Nov 2023

Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks

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

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Latest update: 22 Apr 2024
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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.