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：近年澳大利亚野火导致重大经济损失与财产损毁，气候变化加剧其强度、持续时间和频率的担忧日益增加。极端野火灾害量化是野火管理的重要组成部分，有助于优化资源分配、缓解不利影响及开展灾后恢复工作。然而，尽管极端野火通常最具破坏性，但中小规模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来可靠建模野火蔓延的完整分布。本研究针对1999年至2019年澳大利亚野火新数据集展开分析，以近似对应统计区域一级和二级（SA1/SA2）的空间单元为尺度，对月度蔓延规模进行建模。鉴于野火引燃与蔓延的复杂特性，本文利用统计深度学习和极值理论的最新进展，构建了基于图卷积神经网络与扩展广义帕累托分布的参数化回归模型，从而实现对不规则空间域上观测到的野火蔓延进行建模。我们验证了新提出模型的有效性，并对澳大利亚全域及人口密集社区（包括塔斯马尼亚、悉尼、墨尔本和珀斯）开展了野火灾害评估。