Climate Change Data Portal
| DOI | 10.1016/j.spasta.2024.100811 |
| Deep graphical regression for jointly moderate and extreme Australian wildfires | |
| 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/295728 |
| 作者单位 | King Abdullah University of Science & Technology |
| 推荐引用方式 GB/T 7714 | . Deep graphical regression for jointly moderate and extreme Australian wildfires[J],2024,59. |
| APA | (2024).Deep graphical regression for jointly moderate and extreme Australian wildfires.SPATIAL STATISTICS,59. |
| MLA | "Deep graphical regression for jointly moderate and extreme Australian wildfires".SPATIAL STATISTICS 59(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
