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

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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.
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