Article(id=1281608172381127307, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.03.0362, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760716800000, receivedDateStr=2025-10-18, revisedDate=1766419200000, revisedDateStr=2025-12-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488799423, onlineDateStr=2026-07-08, pubDate=1774627200000, pubDateStr=2026-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488799423, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488799423, creator=13701087609, updateTime=1783488799423, updator=13701087609, issue=Issue{id=1281608117335068953, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='3', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1774627200000', pubDateStr='2026-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488786300, creator='13701087609', updateTime=1783588670896, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027063758533259, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027063758533260, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=203, endPage=211, ext={EN=ArticleExt(id=1281608172599231116, articleId=1281608172381127307, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Optimization model of forest fire spread based on cellular automata, columnId=1277328338121261909, journalTitle=China Safety Science Journal, columnName=Public Safety and Emergency Management, runingTitle=null, highlight=null, articleAbstract=

To investigate the spread characteristics of forest fires under complex topography and multi-factor coupling conditions, this study develops an optimized forest fire spread model that integrates terrain-slope correction, wind-field effects, and vegetation indices. First, Gaussian filtering was applied to correct the digital elevation model (DEM) to reduce noise, and terrain slope and aspect were derived from the refined DEM. Subsequently, the enhanced vegetation index (EVI) was introduced to improve the forest fire spread prediction model, enhancing prediction accuracy in areas with dense vegetation cover. By combining the model with CA, the predicted fire spread can be visualized. Finally, the predicted fire variable values were compared with the observed data from Muli Tibetan Autonomous County to verify the scientific validity and effectiveness of the model. The results indicate that the model is highly sensitive to vegetation changes in low EVI value ranges, with an effect size of 0.870, suggesting that the introduction of EVI improves fire prediction accuracy in areas with high vegetation cover. The improved fire spread model achieved an area prediction error rate and perimeter error rate of 29.40% and 5.79%, respectively, which are lower than the pre-improvement values of 44.27% and 16.99%. The Kappa coefficient of the improved model is 0.8238, which is closer to 1 compared to the pre-improvement model.

, authors=Weihao QIN^{1, 2}, Quanyi LIU^{**, 1, 2}, Hongzhou AI^{1, 2}, Jihao LIU^{1, 2}, Pei ZHU³, authorsList=Weihao QIN, Quanyi LIU, Hongzhou AI, Jihao LIU, Pei ZHU, authorCompany=null, correspAuthors=Quanyi LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608173157073551, articleId=1281608172381127307, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于元胞自动机的森林火灾蔓延优化模型, columnId=1277328338280645463, journalTitle=中国安全科学学报, columnName=公共安全与应急管理, runingTitle=null, highlight=null, articleAbstract=

为探究森林火灾中复杂地形与多因素耦合条件下的蔓延特性，构建集成地形坡度校正、风场影响及植被指数等多维信息的森林火灾蔓延优化模型。首先，利用高斯滤波优化处理数字高程模型（DEM）并根据DEM计算坡度和坡向；然后，引入增强型植被指数（EVI）构建王正非林火蔓延优化模型，提高高植被覆盖区的火灾预测准确性，并通过与元胞自动机（CA）相结合，可视化预测林火蔓延；最后，对比分析木里藏族自治县火灾预测值与真实值，验证该模型的科学性和有效性。结果表明：在低EVI值区间，模型对植被变化非常敏感，且效应量为0.870，表明引入EVI能够在高植被覆盖区提高火灾预测准确性；优化后的林火蔓延模型的面积预测误差率和周长误差率分别为29.40%和5.79%，低于优化前的44.27%和16.99%；优化后的林火蔓延模型的Kappa系数为0.823 8，相比于优化前更接近1。

, authors=覃炜豪^{1, 2}, 刘全义教授^{**, 1, 2}, 艾洪舟副教授^{1, 2}, 刘继豪^{1, 2}, 朱培副教授³, authorsList=覃炜豪, 刘全义教授, 艾洪舟副教授, 刘继豪, 朱培副教授, authorCompany=null, correspAuthors=刘全义教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=A6YtR4S7f652ha+IpKsdsw==, magXml=RTjPxKytYeF/3zj7uVoGGA==, pdfUrl=null, pdf=HGXuhwmHsY4yKtOqy6Ctfw==, pdfFileSize=9340805, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PIv7uLl7UKgkK9z6txkSbg==, mapNumber=null, fund=null)}, authors=[Author(id=1281608175166145178, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608172381127307, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=qwh199911@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608175254225565, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608172381127307, authorId=1281608175166145178, language=EN, stringName=Weihao QIN, firstName=Weihao, middleName=null, lastName=QIN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹College of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
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²中国民用航空飞行学院 民机火灾科学与安全工程四川省重点实验室，四川 广汉 618307, bio={"content":"

覃炜豪（2000—），男，四川德阳人，硕士研究生，主要研究方向为火灾图像分割、森林火灾防控。E-mail: qwh199911@163.com。

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覃炜豪（2000—），男，四川德阳人，硕士研究生，主要研究方向为火灾图像分割、森林火灾防控。E-mail: qwh199911@163.com。

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