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

Bing Wu, Tsz Leung Yip, Xinping Yan, Zhe Mao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)559-580
Number of pages22
JournalJournal of Navigation
Issue number3
Publication statusPublished - 1 May 2020


  • Bayesian Network
  • Consequence Estimation
  • Mutual Information
  • Navigational Accidents

ASJC Scopus subject areas

  • Oceanography
  • Ocean Engineering

Cite this