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A Mutual Information-Based Bayesian Network Model for Consequence Estimation of Navigational Accidents in the Yangtze River

Published online by Cambridge University Press:  19 November 2019

Bing Wu ,
Tsz Leung Yip ,
Xinping Yan  and
Zhe Mao
Bing Wu
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hong Kong, China)
Tsz Leung Yip
Affiliation:
(Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hong Kong, China)
Xinping Yan
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology, Wuhan, China)
Zhe Mao*
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology, Wuhan, China)
*
(E-mail: yanar@whut.edu.cn)
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Abstract

Navigational accidents (collisions and groundings) account for approximately 85% of mari-time accidents, and consequence estimation for such accidents is essential for both emergency resource allocation when such accidents occur and for risk management in the framework of a formal safety assessment. As the traditional Bayesian network requires expert judgement to develop the graphical structure, this paper proposes a mutual information-based Bayesian network method to reduce the requirement for expert judgements. The central premise of the proposed Bayesian network method involves calculating mutual information to obtain the quantitative element among multiple influencing factors. Seven-hundred and ninety-seven historical navigational accident records from 2006 to 2013 were used to validate the methodology. It is anticipated the model will provide a practical and reasonable method for consequence estimation of navigational accidents.

Keywords

Navigational AccidentsConsequence EstimationBayesian NetworkMutual Information

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 559 - 580
Copyright
Copyright © The Royal Institute of Navigation 2019

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