Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level 1 and 2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalised Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth.

澳大利亚最近发生的野火造成了相当大的经济损失和财产破坏，人们越来越担心气候变化可能会加剧野火的强度、持续时间和频率。极端野火的危害量化是野火管理的重要组成部分，因为它有助于有效的资源分配、减轻不利影响和恢复工作。然而，尽管极端野火通常影响最大，但小火和中火仍然可能对当地社区和生态系统造成破坏。因此，必须开发稳健的统计方法来可靠地模拟野火蔓延的完整分布。我们针对 1999 年至 2019 年澳大利亚野火的新数据集进行了此操作，并分析了大约对应于统计区域 1 级和 2 级 (SA1/SA2) 区域的每月蔓延情况。鉴于野火点燃和蔓延的复杂性，我们利用统计深度学习和极值理论的最新进展，使用图卷积神经网络和扩展的广义帕累托分布构建参数回归模型，这使我们能够对在不规则的空间域。我们强调了新提出的模型的有效性，并对澳大利亚和人口密集的社区（即塔斯马尼亚、悉尼、墨尔本和珀斯）进行了野火危险评估。