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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Cited articles

Alcrudo, F. and Garcia-Navarro, P.: A high-resolution Godunov-type scheme in finite volumes for the 2D shallow-water equations, Int. J. Numer. Meth. Fluids, 16, 489–505, 1993. a
Bates, P. D. and De Roo, A. P.: A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000. a
Battaglia, P. W. E. A.: Relational inductive biases, deep learning, and graph networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1806.01261, 2018. a
Bentivoglio, R.: Code repository for paper “Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks”, Zenodo [code], https://doi.org/10.5281/zenodo.10214840, 2023a. a
Bentivoglio, R.: Video simulations for paper “Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks”, Zenodo [video supplement], https://doi.org/10.5281/zenodo.7652663, 2023b. a
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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.