基于随机蔓延过程的野火空间演化模型

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Environmetrics Pub Date : 2022-10-24 DOI:10.1002/env.2774
Carlos Díaz-Avalos, Pablo Juan
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引用次数: 0

摘要

研究野火的蔓延和燃烧面积的增长是生态学研究和其他领域的一项重要任务。在这项工作中,我们提出了一个火灾蔓延的模型,并展示了从燃烧区域模拟得到的结果。该模型基于不同地点发生火灾的概率。这些概率是由空间点过程模型的强度函数得到的,该模型拟合了1993-2015年瓦伦西亚社区观测到的火灾模式。这些模型应用于西班牙不同的野火,包括不同的时间状态,将网络模型的特征与燃烧和非燃烧细胞之间相互作用的准物理模型的特征结合起来,这些模型强烈依赖于协变量。模拟的野火燃烧区域的结果与在真实情况下观察到的燃烧区域相似,这表明基于称为随机扩散过程的马尔可夫过程的模型是有效的。该模型可以扩展到模拟其他随机传播过程,如流行病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modeling the spatial evolution wildfires using random spread process

The study of wildfire spread and the growth of the area burned is an important task in ecological studies and in other contexts. In this work we present a model for fire spread and show the results obtained from simulations of burned areas. The model is based on probabilities of fire at different locations. Such probabilities are obtained from the intensity function of a spatial point process model fitted to the observed pattern of fires in the Valencian Community for the years 1993–2015. The models, applied to different wildfires in Spain, including the different temporal states, combines the features of a network model with those of a quasi-physical model of the interaction between burning and nonburning cells, which strongly depends on covariates. The results of the simulated wildfire burned areas resemble the burned areas observed in real cases, suggesting that the model proposed, based on a Markov process called Random Spread Process, works adequately. The model can be extended to simulate other random spread processes such as epidemics.

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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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