Article(id=1282037399425364787, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.0732, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752940800000, receivedDateStr=2025-07-20, revisedDate=1760803200000, revisedDateStr=2025-10-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591135126, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591135126, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591135126, creator=13701087609, updateTime=1783591135126, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=139, endPage=146, ext={EN=ArticleExt(id=1282037399685411636, articleId=1282037399425364787, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Inversion of source information in multi-factor optimized Gaussian model by using chaotic mapping-based adaptive firefly algorithm, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To qucikly obtain the source strength and location information from hazardous gas leakage incidents, a multi-factor optimized Gaussian plume dispersion model was proposed, and the leakage source parameters were inverted by combining it with a CAFA. Key environmental factors such as wind speed distribution, surface resistance, and surface reflection were incorporated into the Gaussian plume model to enhance its fitting capability under complex conditions through multi-factor calibration. Furthermore, a chaotic mapping was introduced to improve the population diversity and global search ability of firefly algorithm(FA), thereby achieving an effective balance between global optimization and local refinement while reducing the risk of falling into local optima. The results indicate that, after optimization based on wind speed distribution, surface resistance, and surface reflection, the error of the Gaussian plume model is reduced by 16%. CAFA effectively can avoid falling into local optima, reducing the source strength inversion error from 63.56% to 0.22%, and the leak source coordinate inversion error from 1.5 m to 0.2 m.

, authors=Jinyu LUO^{1, 2}, Yongqiang WANG², Shengzhu ZHANG³, Limin DENG^{1, 4}, Minjun PENG^{1, 4}, Niansheng KUAI^{**, 1, 4}, authorsList=Jinyu LUO, Yongqiang WANG, Shengzhu ZHANG, Limin DENG, Minjun PENG, Niansheng KUAI, authorCompany=null, correspAuthors=Niansheng KUAI, 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=1282037400150979383, articleId=1282037399425364787, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于混沌映射自适应萤火虫算法的多参优化高斯模型反演, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为快速获取危险气体泄漏事故中包含的源强与位置信息，提出一种基于多因素优化的高斯烟羽扩散模型，并结合混沌映射自适应萤火虫算法（CAFA）反演泄漏源参数。将风速分布、地表阻力及地表反射等关键环境因子引入高斯烟羽模型，通过多因素校正提升模型对复杂工况的拟合能力；在此基础上引入混沌映射以增强萤火虫算法（FA）的种群多样性与全局搜索能力，从而在全局寻优与局部精化之间实现有效平衡，降低陷入局部最优的风险。结果表明：基于风速分布、地面阻力和地面反射优化后，高斯烟羽模型的误差降低16%。CAFA算法能有效避免陷入局部最优解，源强反演的误差可从63.56%降低至0.22%，泄漏源坐标反演的误差可从1.5 m降低至0.2 m。

, authors=罗晋宇^{1, 2}, 王永强副教授², 张圣柱正高级工程师³, 邓利民正高级工程师^{1, 4}, 彭敏君高级工程师^{1, 4}, 蒯念生高级工程师^{**, 1, 4}, authorsList=罗晋宇, 王永强副教授, 张圣柱正高级工程师, 邓利民正高级工程师, 彭敏君高级工程师, 蒯念生高级工程师, authorCompany=null, correspAuthors=蒯念生高级工程师, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Xk1xZQorkKbVPWLj8emdZA==, magXml=BdePEZMv4x0zSQflh77gMA==, pdfUrl=null, pdf=ISTqkTgu4jZQ/MlbTUVQgQ==, pdfFileSize=4300217, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=LOBz8Vius31BrG638OTV7g==, mapNumber=null, fund=null)}, authors=[Author(id=1282037400805290821, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037399425364787, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=sakuraka2024@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282037400889176904, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037399425364787, authorId=1282037400805290821, language=EN, stringName=Jinyu LUO, firstName=Jinyu, middleName=null, lastName=LUO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹Sichuan Institute of Safety Science and Technology, Chengdu Sichuan 610045, China
²School of Environment and Resources, Southwest University of Science and Technology, Mianyang Sichuan 621010, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282037400956285769, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037399425364787, authorId=1282037400805290821, language=CN, stringName=罗晋宇, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹四川省安全科学技术研究院，四川 成都 610045
²西南科技大学 环境与资源学院，四川 绵阳 621010, bio={"content":"

罗晋宇（2001—），男，陕西西安人，硕士研究生，研究方向为化工园区危险气体源信息反演。E-mail: sakuraka2024@163.com。

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罗晋宇（2001—），男，陕西西安人，硕士研究生，研究方向为化工园区危险气体源信息反演。E-mail: sakuraka2024@163.com。

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