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

Viewed

Total article views: 4,943 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,652 1,214 77 4,943 77 59
  • HTML: 3,652
  • PDF: 1,214
  • XML: 77
  • Total: 4,943
  • BibTeX: 77
  • EndNote: 59
Views and downloads (calculated since 22 Mar 2023)
Cumulative views and downloads (calculated since 22 Mar 2023)

Viewed (geographical distribution)

Total article views: 4,943 (including HTML, PDF, and XML) Thereof 4,751 with geography defined and 192 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jan 2025
Download
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.