Understanding the behavior of extreme environmental events is crucial for evaluating economic losses, assessing risks, and providing health care, among many other related aspects. In a spatial context, relevant for environmental events, the dependence structure is extremely important, influencing joint extreme events and extrapolating on them. Thus, recognizing or at least having preliminary information on the 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 versus asymptotic independence, using a convolutional neural network (CNN). We designed a CNN architecture as an efficient classifier of a dependence structure. Extremal dependence measures are used to train the CNN. We tested our methodology on simulated and real datasets: air temperature data at 2 m over Iraq and rainfall data along the east coast of Australia.

中文翻译：

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识别空间极端依赖结构：一种深度学习方法

了解极端环境事件的行为对于评估经济损失、评估风险和提供医疗保健以及许多其他相关方面至关重要。在与环境事件相关的空间背景下，依赖结构非常重要，影响联合极端事件并对其进行推断。因此，识别或至少获得有关这些依赖结构模式的初步信息是理解极端事件的宝贵知识。在这项研究中，我们使用卷积神经网络 (CNN) 解决空间渐近依赖与渐近独立的自动识别问题。我们设计了一个 CNN 架构作为依赖结构的有效分类器。极端依赖测量用于训练 CNN。