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 generalized 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.

翻译：近年澳大利亚野火造成巨大经济损失和财产破坏，人们日益担忧气候变化可能加剧野火的强度、持续时间和频率。极端野火的灾害量化是野火管理的重要组成部分，有助于实现资源高效分配、减轻不利影响和开展恢复工作。然而，尽管极端野火通常影响最大，但小型和中度火灾仍可能对当地社区和生态系统造成毁灭性打击。因此，亟需开发稳健的统计方法，可靠地模拟野火蔓延的全部分布特征。我们针对1999年至2019年澳大利亚野火的新数据集展开研究，分析大致对应统计一级和二级区域（SA1/SA2）的月度蔓延范围。鉴于野火引燃和蔓延的复杂性，我们利用统计深度学习与极值理论的最新进展，构建了基于图卷积神经网络和扩展广义帕累托分布的参数回归模型，从而能够模拟在不规则空间域上观测到的野火蔓延。我们验证了新提出模型的有效性，并对澳大利亚全境及人口密集社区（塔斯马尼亚、悉尼、墨尔本和珀斯）进行了野火灾害评估。