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In response to the frequent and high-impact accidents in key marine areas, a risk assessment model for accidents in such areas based on TAN network was established. To address the issue of partial sample bias in accident reporting, the boxplot method was employed to eliminate outliers and improve data quality. Considering the complexity and correlation of risk factors, a random forest algorithm was utilized to identify key risk factors and establish a risk evaluation index system for accidents in key marine areas. In addition, the performance of TAN network model was compared with six machine learning models for validation and analysis. The results demonstrate that TAN network achieves the highest accuracy of 93.02%. The findings indicate that ship speed, ship length, and pirate attacks are the primary factors contributing to risk events in key marine areas. Vessels aged between 11 and 20 years should be prioritized for maintenance and inspection. In addition, ships navigating in shallow key marine areas should operate with increased caution.

, authors=Jing LYU, Zixin REN^**, Hanwen FAN, Zheng CHANG, authorsList=Jing LYU, Zixin REN, Hanwen FAN, Zheng CHANG, authorCompany=null, correspAuthors=Zixin REN, 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=1282037418966627306, articleId=1282037418484282344, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于树增强朴素贝叶斯网络的关键海域事故风险评价模型, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为应对关键海域事故频发且危害较大的问题，构建一种基于树增强朴素贝叶斯（TAN）网络的关键海域事故风险评价模型；为克服事故报告中存在部分样本偏差的缺陷，利用箱线图方法消除异常值，以提升数据质量；考虑风险因素复杂性以及相关性特征，利用随机森林算法，识别出关键风险因素，并构建关键海域事故风险评价指标体系；此外，将TAN网络模型与6种机器学习模型对比，进行验证分析。结果表明：TAN网络模型的准确率最优，为93.02%；船速、船舶长度、海盗袭击是诱发关键海域风险事件的主要因素；船龄在11~20年的船舶应作为重点维修与检验对象；在水深较浅的关键海域航行的船舶应加强操作谨慎性。

, authors=吕靖教授, 任梓昕^**, 范瀚文, 常征副教授, authorsList=吕靖教授, 任梓昕, 范瀚文, 常征副教授, authorCompany=null, correspAuthors=任梓昕, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vG3Fsalg0VjNlaK8S7aA4g==, magXml=NDrCoKiwyQGtzAPKTRm13w==, pdfUrl=null, pdf=qfk2+h+lEIG5VLZOunJstg==, pdfFileSize=5886481, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=zX6D1FY0xoFF63VFwKeYcg==, mapNumber=null, fund=null)}, authors=[Author(id=1282037419285394415, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037418484282344, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=lujing@dlmu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282037419360891889, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037418484282344, authorId=1282037419285394415, language=EN, stringName=Jing LYU, firstName=Jing, middleName=null, lastName=LYU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Transportation Engineering, Dalian Maritime University, Dalian Liaoning 116026, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282037419423806450, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037418484282344, authorId=1282037419285394415, language=CN, stringName=吕靖教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=大连海事大学 交通运输工程学院，辽宁 大连 116026, bio={"content":"

吕靖（1959—），男，黑龙江五常人，硕士，教授，主要从事交通运输规划与管理方面的研究。E-mail: lujing@dlmu.edu.cn。

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吕靖（1959—），男，黑龙江五常人，硕士，教授，主要从事交通运输规划与管理方面的研究。E-mail: lujing@dlmu.edu.cn。

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