Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 5
Articles | Volume 26, issue 5
https://doi.org/10.5194/nhess-26-2089-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-2089-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 5
Research article
 | 
08 May 2026
Research article |  | 08 May 2026

Probabilistic flood hazard mapping for dike-breach floods via graph neural networks

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

Viewed

Total article views: 5,814 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,526 2,081 207 5,814 793 814
  • HTML: 3,526
  • PDF: 2,081
  • XML: 207
  • Total: 5,814
  • BibTeX: 793
  • EndNote: 814
Views and downloads (calculated since 19 Dec 2025)
Cumulative views and downloads (calculated since 19 Dec 2025)

Viewed (geographical distribution)

Total article views: 5,814 (including HTML, PDF, and XML) Thereof 5,706 with geography defined and 108 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 03 Aug 2026
Download
Short summary
Obtaining probabilistic flood maps with numerical models is very time-consuming. Deep learning models can speed this up, but their predictions are difficult to verify without reference data, and they ignore structures like dikes or canals. This work introduces a mass-based validation measure to assess prediction plausibility and adapts a graph-based model to include hydraulic structures, enabling realistic, large-scale probabilistic flood mapping in the Netherlands.
Read more
Share
Altmetrics
Final-revised paper
Preprint