Article(id=1240656098546471872, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.01.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1697472000000, receivedDateStr=2023-10-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773725064168, onlineDateStr=2026-03-17, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773725064168, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773725064168, creator=13701087609, updateTime=1773725064168, updator=13701087609, issue=Issue{id=1240656096998773667, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='1', pageStart='1', pageEnd='198', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773725063800, creator=13701087609, updateTime=1773725118083, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656324749480238, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656324749480239, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=18, endPage=25, ext={EN=ArticleExt(id=1240656098882016211, articleId=1240656098546471872, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on offshore wind speed prediction based on Adaboost algorithm, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

Complex meteorological sea conditions directly affect the safety of ship navigation, and the accuracy of the prediction of offshore wind speed, as a major factor in meteorological sea conditions, is of great significance to the navigation safety and trajectory planning. In order to effectively improve the accuracy of offshore wind speed prediction and overcome the limitations of a single prediction model, the offshore wind form data of Lianyungang station is used as an example study, and the Adaboost algorithm is used to integrate the advantages of multi-models to construct a combined prediction model of offshore wind speed. Four time series prediction models, including BP neural network, GA BPNN, long and short-term memory network and WOA-SVR, are used for wind speed prediction. Considering the prediction effect of a single model, Adaboost algorithm is applied to integrate the GA-BPNN model and WOA-SVR model to construct the combined offshore wind speed prediction model, and the integration accuracy is compared with that of Bagging algorithm. The results show that the root mean square error of the combined prediction model with the Adaboost algorithm is reduced by about 13% and the mean absolute error is reduced by about 16% compared with the single model, which effectively verifies the superiority of the combined prediction model in the prediction of offshore wind speed data, and it is of great significance for the enhancement of navigational safety and the optimization of the trajectory design.

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复杂气象海况条件直接影响船舶的航海安全，海上风速作为气象海况中的主要因素，其预测的精准性对航行安全以及航迹规划等具有重要意义。为有效提高海上风速预测的精准度，克服单一预测模型的局限性，对连云港站点海上风场数据进行实例研究，采用Adaboost集成算法融合多模型优势构建海上风速组合预测模型。分别采用BP神经网络（Back Propagation Neural Network, BPNN）、遗传算法（Genetic Algorithm, GA）-BPNN、长短期记忆网络（Long Short-Term Memory, LSTM）和鲸鱼优化算法（Whale Optimization Algorithm, WOA）-支持向量回归（Support Vector Regression, SVR）等4种时间序列预测模型进行风速预测。考虑单一模型预测效果，应用Adaboost算法对GA-BPNN模型和WOA-SVR模型进行集成，进而构建海上风速组合预测模型，并与Bagging算法集成精度进行比较。分析结果表明:Adaboost集成算法的组合预测模型均方根误差相较单一模型均方根误差降低了约13%，平均绝对误差降低了约16%，试验结果有效地验证了组合预测模型在海上风速数据预测方面的优越性，对提高航海安全性与航迹优化设计具有重要的指导意义。

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张润锋（1993—），男，讲师，博士，研究方向为海洋航行器安全与环境感知。E-mail: zhangrunfeng@tju.edu.cn

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张润锋（1993—），男，讲师，博士，研究方向为海洋航行器安全与环境感知。E-mail: zhangrunfeng@tju.edu.cn

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