CSIRO Data61 , Hobart, Tasmania 7001, Australia.
School of Medicine, University of Tasmania , Hobart, Tasmania 7000, Australia.
R Soc Open Sci. 2016 Feb 10;3(2):150241. doi: 10.1098/rsos.150241. eCollection 2016 Feb.
Increasing Australian bush-fire frequencies over the last decade has indicated a major climatic change in coming future. Understanding such climatic change for Australian bush-fire is limited and there is an urgent need of scientific research, which is capable enough to contribute to Australian society. Frequency of bush-fire carries information on spatial, temporal and climatic aspects of bush-fire events and provides contextual information to model various climate data for accurately predicting future bush-fire hot spots. In this study, we develop an ensemble method based on a two-layered machine learning model to establish relationship between fire incidence and climatic data. In a 336 week data trial, we demonstrate that the model provides highly accurate bush-fire incidence hot-spot estimation (91% global accuracy) from the weekly climatic surfaces. Our analysis also indicates that Australian weekly bush-fire frequencies increased by 40% over the last 5 years, particularly during summer months, implicating a serious climatic shift.
过去十年中,澳大利亚丛林火灾频率的增加表明未来将发生重大气候变化。对澳大利亚丛林火灾的这种气候变化的理解是有限的,迫切需要有足够能力为澳大利亚社会做出贡献的科学研究。丛林火灾的频率携带着丛林火灾事件的空间、时间和气候方面的信息,并为各种气候数据建模提供上下文信息,以便准确预测未来的丛林火灾热点。在这项研究中,我们开发了一种基于两层机器学习模型的集成方法,以建立火灾发生率与气候数据之间的关系。在 336 周的数据试验中,我们证明该模型能够从每周的气候表面高度准确地预测丛林火灾发生率热点(全球准确率为 91%)。我们的分析还表明,过去 5 年来,澳大利亚每周的丛林火灾频率增加了 40%,特别是在夏季,这意味着气候发生了严重转变。