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

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

doi:10.5194/nhess-26-2089-2026

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

Data sets

mSWE-GNN: version for paper “Prob- abilistic flood hazard mapping for dike-breach floods via graph neural networks” In Natural Hazards and Earth System Sciences R. Bentivoglio https://doi.org/10.5281/zenodo.20053958

Model code and software

mSWE-GNN: version for paper “Prob- abilistic flood hazard mapping for dike-breach floods via graph neural networks” In Natural Hazards and Earth System Sciences R. Bentivoglio https://doi.org/10.5281/zenodo.20053958

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.