| 基于SVR的船舶航行安全评估模型 |
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| 引用本文： | 常婧,柳晓鸣,李梦蕊.基于SVR的船舶航行安全评估模型[J].上海海事大学学报,2020,41(2):21-26. |
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| 作者姓名： | 常婧 柳晓鸣 李梦蕊 |
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| 作者单位： | 大连海事大学信息科学技术学院,辽宁 大连 116026;大连海事大学信息科学技术学院,辽宁 大连 116026;大连海事大学信息科学技术学院,辽宁 大连 116026 |
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| 摘 要： | 为对船舶航行安全状况进行有效预测,利用支持向量回归(support vector regression,SVR)算法构建船舶航行安全评估模型。在分析影响船舶航行安全的因素的基础上,对船舶历史数据进行预处理后将其作为模型训练和测试的样本数据。实验结果表明:该模型评估准确度可达99.6%以上;在同一样本数据条件下,模型的评估准确度和稳定性均优于基于极限学习机(extreme learning machine,ELM)构建的模型。模型的评估结果为水上交通管理部门的监管提供参考。
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| 关 键 词： | 航行安全 安全评估 支持向量回归(SVR) 极限学习机(ELM) |
| 收稿时间： | 2019/5/23 0:00:00 |
| 修稿时间： | 2019/10/27 0:00:00 |
Ship navigation safety assessment model based on SVR |
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| CHANG Jing,LIU Xiaoming,LI Mengrui.Ship navigation safety assessment model based on SVR[J].Journal of Shanghai Maritime University,2020,41(2):21-26. |
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| Authors: | CHANG Jing LIU Xiaoming LI Mengrui |
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| Institution: | (Information Science and Technology College,Dalian Maritime University,Dalian 116026,Liaoning,China) |
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| Abstract: | In order to effectively predict the ship navigation safety status, the ship navigation safety assessment model is constructed by the support vector regression (SVR) algorithm. Based on the analysis of the factors affecting ship navigation safety, the ship historical data are preprocessed and used as sample data for model training and testing. The experimental results show that: the accuracy of the model evaluation can reach more than 99.6%; under the same sample data conditions, the accuracy and stability of the model are better than those constructed based on the extreme learning machine (ELM). The assessment results of the model provide reference for the supervision of the water traffic management department. |
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| Keywords: | navigation safety safety assessment support vector regression (SVR) extreme learning machine (ELM) |
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