Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 4
Articles | Volume 26, issue 4
https://doi.org/10.5194/nhess-26-1663-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-1663-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 4
Research article
 | 
14 Apr 2026
Research article |  | 14 Apr 2026

Simulating spatial multi-hazards with generative deep learning

Alison Peard, Yu Mo, and Jim W. Hall

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Short summary
We developed a generative deep learning method combining generative adversarial networks and extreme value theory to simulate spatially resolved multi-hazards events across large regions. Tested on storms in the Bay of Bengal, the model captured spatial patterns of wind, pressure, and rainfall during storm events, enabling more realistic disaster risk assessments than traditional methods. This flexible framework can be applied to various hazards and regions for improved disaster planning.
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