Article(id=1311360881901007180, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758729600000, receivedDateStr=2025-09-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582398152, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582398152, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582398152, creator=13701087609, updateTime=1790582398152, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=76, endPage=87, ext={EN=ArticleExt(id=1311360882093945165, articleId=1311360881901007180, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship fuel consumption prediction model based on feature selection method, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

With the continuous enhancement of global shipping's requirements for energy conservation and emission reduction, accurate prediction of ship fuel consumption has become crucial for optimizing shipping efficiency and mitigating environmental impacts. However, the issues of feature redundancy and noise interference in multi-source data restrict the accuracy and stability of fuel consumption prediction models. To address these problems, a comprehensive framework for precise fuel consumption prediction is proposed. First, ship noon report data, ERA5 reanalysis data, global ocean physical analysis and prediction data, and Automatic Identification System (AIS) data are integrated, and a multi-dimensional feature space dataset is constructed. Second, to address feature redundancy and noise interference, a genetic algorithm is introduced for feature selection, and the original 32 features are reduced to 15. Third, nine machine learning regression models are systematically evaluated, and the results indicate that the Random Forest model exhibits significant advantages in capturing the nonlinear characteristics of fuel consumption. Finally, feature importance analysis based on the RF model reveals that time-related variables (such as daily operating hours and total operating hours) play a dominant role in fuel consumption prediction, providing quantitative decision support for energy efficiency management. The results show that the genetic algorithm-based feature selection effectively reduces computational complexity by approximately 53.1%, and the Random Forest model performs best in capturing the nonlinear characteristics of fuel consumption, significantly improving prediction accuracy. The research findings provide theoretical support for fuel optimization and emission reduction strategies in the shipping industry, and are of great significance in promoting the green transformation of maritime transport.

, authors=Jian Wang¹, Xinglong Wang¹, Wei Ji¹, Wenjie Cao², Zhongming Xiao², Xinjian Wang^{2, *}, authorsList=Jian Wang, Xinglong Wang, Wei Ji, Wenjie Cao, Zhongming Xiao, Xinjian Wang, authorCompany=null, correspAuthors=Xinjian Wang, 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=1311360882475626830, articleId=1311360881901007180, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于特征选择方法的船舶燃油消耗预测模型, columnId=1240655020820067227, journalTitle=中国航海, columnName=绿色航运, runingTitle=null, highlight=null, articleAbstract=

随着全球航运业对节能减排要求的不断提高，精确预测船舶燃油消耗已成为优化航运效率与降低环境影响的关键。然而，多源数据中存在的特征冗余与噪声干扰问题，制约了燃油消耗预测模型的精度与稳定性。针对上述问题，提出一种精确预测船舶燃油消耗的综合框架。整合船舶正午报告数据、欧洲中期天气预报中心（ERA5）分析数据、全球海洋物理分析与预测数据以及自动识别系统数据，构建了多维特征空间数据；针对特征冗余与噪声干扰问题，引入遗传算法进行特征选择，将32个原始特征精简至15个；然后，系统评估九种机器学习回归模型，结果显示随机森林（RF）在捕捉燃油消耗非线性特性方面具有显著优势；基于RF模型的特征重要性分析揭示了时间维度变量（如日航时、总航时）在燃油消耗预测中的主导作用，为能效管理提供了定量决策依据。结果表明:遗传算法特征选择有效降低了约53.1%的计算复杂度，随机森林模型在捕捉燃油消耗非线性特性方面表现最佳，显著提升了预测精度。研究成果为船舶航运业的燃油优化和减排策略制定提供了理论支撑，对推动航运业绿色转型具有重要意义。

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王舰（1977—），男，工程师，研究方向为船舶能效管理，（E-mail）wangxinjian@dlmu.edu.cn

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王舰（1977—），男，工程师，研究方向为船舶能效管理，（E-mail）wangxinjian@dlmu.edu.cn

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