气候变化领域集成服务门户(CCPortal): Deep graphical regression for jointly moderate and extreme Australian wildfires

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DOI	10.1016/j.spasta.2024.100811
	Deep graphical regression for jointly moderate and extreme Australian wildfires
	Cisneros, Daniela; Richards, Jordan; Dahal, Ashok; Lombardo, Luigi; Huser, Raphael
发表日期	2024
ISSN	2211-6753
起始页码	59
卷号	59
英文摘要	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.
英文关键词	Extended generalised pareto distribution; Extreme value theory; Graph convolutional neural networks; Parametric regression; Wildfire burnt area; Wildfire spread
语种	英语
WOS研究方向	Geology ; Mathematics ; Remote Sensing
WOS类目	Geosciences, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Remote Sensing ; Statistics & Probability
WOS记录号	WOS:001167497900001
来源期刊	SPATIAL STATISTICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295729
作者单位	King Abdullah University of Science & Technology
推荐引用方式 GB/T 7714	Cisneros, Daniela,Richards, Jordan,Dahal, Ashok,et al. Deep graphical regression for jointly moderate and extreme Australian wildfires[J],2024,59.
APA	Cisneros, Daniela,Richards, Jordan,Dahal, Ashok,Lombardo, Luigi,&Huser, Raphael.(2024).Deep graphical regression for jointly moderate and extreme Australian wildfires.SPATIAL STATISTICS,59.
MLA	Cisneros, Daniela,et al."Deep graphical regression for jointly moderate and extreme Australian wildfires".SPATIAL STATISTICS 59(2024).