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

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

doi:https://doi.org/10.5194/hess-27-4227-2023

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.

https://doi.org/10.5194/hess-27-4227-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 23

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

Data sets

Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks" R. Bentivoglio and R. Bruijns https://doi.org/10.5281/zenodo.7764418

Model code and software

Code repository for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks" R. Bentivoglio https://doi.org/10.5281/zenodo.10214840

SWE-GNN-paper-repository- RBTV1 https://github.com/RBTV1/SWE-GNN-paper-repository-

Video supplement

Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks" R. Bentivoglio https://doi.org/10.5281/zenodo.7652663

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.