基于互信息的长江船舶碰撞险情等级预测方法

doi:10.16265/j.cnki.issn1003-3033.2018.11.027

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (11): 168-175.doi: 10.16265/j.cnki.issn1003-3033.2018.11.027

基于互信息的长江船舶碰撞险情等级预测方法

陈克嘉^1,2, 毛喆^1,2 副研究员, 吴兵^1,2 助理研究员, 范莘^1,2

1 武汉理工大学智能交通系统研究中心,湖北武汉 430063
2 国家水运安全工程技术研究中心,湖北武汉 430063

收稿日期:2018-08-09 修回日期:2018-10-13 发布日期:2020-11-25
作者简介:陈克嘉 (1994—),女,浙江绍兴人,硕士研究生,研究方向为水上交通安全和预警。
基金资助:
湖北省自然科学基金资助(2017CFB202);工信部高技术船舶科研项目。

Mutual information based prediction of level of collision incident in Yangtze river

CHEN Kejia, MAO Zhe, WU Bing, FAN Shen

1 Intelligent Transportation System Research Center, Wuhan University of Technology, Wuhan Hubei 430063, China
2 National Engineering Research Center for Water Transport Safety, Wuhan Hubei 430063, China

Received:2018-08-09 Revised:2018-10-13 Published:2020-11-25

摘要/Abstract

摘要： 为预测船舶碰撞险情等级、评价碰撞事故后果,利用互信息改进传统贝叶斯网络(BN),建立船舶碰撞险情等级预测模型。首先,基于船舶碰撞事故历史数据对船舶碰撞险情等级的风险因素进行识别;其次,计算互信息和条件互信息值判断风险因素之间独立性和条件独立性,确定因素之间依赖关系,确立BN定性部分,进而依据516条事故数据得到条件概率表(CPT),确定BN的定量部分;最后,应用长江历史事故数据验证模型的可行性和精确性。研究表明:经改进的预测模型预测精准度达94%,能够很好地预测船舶碰撞险情等级。

关键词: 船舶碰撞, 风险因素, 风险等级预测模型, 互信息, 贝叶斯网络(BN)

Abstract: In order to predict the level of ship collision dangers and evaluate the consequences of collisions, the mutual information was used to improve the traditional BN and a prediction model of ship collision risk grades was built. Firstly, the risk factors of ship collision danger grades were identified based on the historical data on ship collision accidents. Second, the qualitative part of the BN was established by the mutual information and conditional mutual information values calculated to determine the independence and independence of conditions between risk factors, and the dependencies between the factors were determined. According to data on the 516 accidents, a conditional probability table(CPT) was obtained to determine the quantitative part of BN. Finally, the feasibility and accuracy of the model were validated by using the data on the Yangtze River historical accidents. The result shows that the prediction accuracy obtained using model is up to 94%, which can well predict the ship collision risk level.

Key words: ship collision, risk factor, risk rating forecast model, mutual information, Bayesian network (BN)

中图分类号:

X928.03

陈克嘉, 毛喆, 吴兵, 范莘. 基于互信息的长江船舶碰撞险情等级预测方法[J]. 中国安全科学学报, 2018, 28(11): 168-175.

CHEN Kejia, MAO Zhe, WU Bing, FAN Shen. Mutual information based prediction of level of collision incident in Yangtze river[J]. China Safety Science Journal, 2018, 28(11): 168-175.

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基于互信息的长江船舶碰撞险情等级预测方法

Mutual information based prediction of level of collision incident in Yangtze river

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