Recognizing a Spatial Extreme dependence structure: A Deep Learning approach

TL;DR

This paper introduces a CNN-based method to automatically classify spatial dependence structures of environmental extreme events, distinguishing between asymptotic dependence and independence using tail dependence measures.

Contribution

It develops a novel CNN architecture trained on tail dependence measures to recognize spatial dependence patterns in environmental data.

Findings

Effective classification of dependence structures achieved.

Method validated on simulated and real environmental datasets.

Potential for improved risk assessment and environmental modeling.

Abstract

Understanding the behaviour of environmental extreme events is crucial for evaluating economic losses, assessing risks, health care and many other aspects. In the spatial context, relevant for environmental events, the dependence structure plays a central rule, as it influence joined extreme events and extrapolation on them. So that, recognising or at least having preliminary informations on patterns of these dependence structures is a valuable knowledge for understanding extreme events. In this study, we address the question of automatic recognition of spatial Asymptotic Dependence (AD) versus Asymptotic independence (AI), using Convolutional Neural Network (CNN). We have designed an architecture of Convolutional Neural Network to be an efficient classifier of the dependence structure. Upper and lower tail dependence measures are used to train the CNN. We have tested our methodology on simulated and real data sets: air temperature data at two meter over Iraq land and Rainfall data in the east cost of Australia.