基于随机元胞自动机和潜在时空动力学的野火传播数据驱动模型

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2023-11-10 DOI:10.1016/j.spasta.2023.100794

Nicholas Grieshop, Christopher K. Wikle

{"title":"基于随机元胞自动机和潜在时空动力学的野火传播数据驱动模型","authors":"Nicholas Grieshop, Christopher K. Wikle","doi":"10.1016/j.spasta.2023.100794","DOIUrl":null,"url":null,"abstract":"<div><p>We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure that allows neighbor states to inform transition probabilities in a multistate categorical model. Additional spatial information is captured by the use of a temporally evolving latent spatio-temporal dynamic process linked to the original spatial domain by spatial basis functions. The Bayesian construction allows for uncertainty quantification associated with each of the predicted fire states. The approach is applied to a heavily instrumented controlled burn.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000696/pdfft?md5=85012020bbc951cf86996eaf31c9c76f&pid=1-s2.0-S2211675323000696-main.pdf","citationCount":"1","resultStr":"{\"title\":\"Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics\",\"authors\":\"Nicholas Grieshop, Christopher K. Wikle\",\"doi\":\"10.1016/j.spasta.2023.100794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure that allows neighbor states to inform transition probabilities in a multistate categorical model. Additional spatial information is captured by the use of a temporally evolving latent spatio-temporal dynamic process linked to the original spatial domain by spatial basis functions. The Bayesian construction allows for uncertainty quantification associated with each of the predicted fire states. The approach is applied to a heavily instrumented controlled burn.</p></div>\",\"PeriodicalId\":48771,\"journal\":{\"name\":\"Spatial Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2211675323000696/pdfft?md5=85012020bbc951cf86996eaf31c9c76f&pid=1-s2.0-S2211675323000696-main.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spatial Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211675323000696\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial Statistics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211675323000696","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 1

摘要

本文提出了一种贝叶斯随机元胞自动机建模方法，以不确定性量化野火的蔓延。该模型考虑了一种动态邻域结构，允许邻域状态通知多状态分类模型中的转移概率。附加的空间信息是通过空间基函数与原始空间域相关联的时间演变的潜在时空动态过程来捕获的。贝叶斯构造允许与每个预测的火灾状态相关联的不确定性量化。该方法应用于重度仪器控制烧伤。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure that allows neighbor states to inform transition probabilities in a multistate categorical model. Additional spatial information is captured by the use of a temporally evolving latent spatio-temporal dynamic process linked to the original spatial domain by spatial basis functions. The Bayesian construction allows for uncertainty quantification associated with each of the predicted fire states. The approach is applied to a heavily instrumented controlled burn.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.00

自引率

21.70%

发文量

审稿时长

55 days

期刊介绍： Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.