# Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks

@inproceedings{Boulaguiem2021ModellingAS, title={Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks}, author={Younes Boulaguiem and Jakob Zscheischler and Edoardo Vignotto and Karin Wiel and Sebastian Engelke}, year={2021} }

Modelling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extreme value theory is often used to model spatial extremes. However, most commonly used approaches require strong assumptions and are either too simplistic or over-parameterized. From a machine learning perspective, Generative Adversarial Networks (GANs) are a powerful tool to model dependencies in high-dimensionalβ¦Β

## One Citation

Simulating flood event sets using extremal principal components

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Hazard event sets, which correspond to a collection of synthetic flood events, are an important tool for practitioners to analyse and manage future flood risks. In this paper, we address the issue ofβ¦

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