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.

In this paper, we consider isotropic and stationary max-stable, inverse max-stable and max-mixture processes X = (X(s)) s∈R 2 and the damage function D ν X = |X| ν with 0 < ν < 1/2. We study the quantitative behavior of a risk measure which is the variance of the average of D ν X over a region A ⊂ R 2 . This kind of risk measure has already been introduced and studied for some max-stable processes in [14]. We evaluated the proposed risk measure by a simulation study.