.png){kind=icon}
.png){kind=icon}
Return
Simulating spatial multi-hazards with generative deep learning
.png){kind=icon}
OA
AI
DOI：10.5194/nhess-26-1663-2026.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Abstract. When natural hazards coincide or spread across large areas they can create major disasters. For accurate risk analysis; it is necessary to simulate many spatially resolved hazard events that capture the relationships between extreme variables; but this has proved challenging for conventional statistical methods; particularly in high-dimensional settings. In this article we show that generative deep learning models – when combined with specific transformations to the training data – offer a useful alternative method for stochastically sampling realistic multi-hazard events. Our framework combines generative adversarial networks with extreme value theory in a hybrid approach that can capture complex dependence structures in gridded multivariate weather data; while providing a theoretical basis for extrapolation to new extremes. We apply our method to jointly model fields of strong winds; heavy precipitation; and low atmospheric pressure (∼ 12 000 variables) during storms in the Bay of Bengal; demonstrating that our model learns the spatial and multivariate extremal dependence structures of the underlying data and captures the distribution of storm severities. For the Bay of Bengal case study; we validate our approach against a popular model for multivariate climate extremes; and demonstrate improved performance in capturing the extremal correlation structure.
Keywords:
generative deep learning
multi-hazard simulation
extreme value theory
spatial dependence
climate extremes
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
N
IF:
4.7
Papers:
4.5K
Citations:
1.4W