Data-driven Bayesian network for risk analysis of global maritime accidents
Data-driven Bayesian network for risk analysis of global maritime accidents
Maritime risk research often suffers from insufficient data for accurate prediction and analysis. This paper aims to conduct a new risk analysis by incorporating the latest maritime accident data into a Bayesian network (BN) model to analyze the key risk influential factors (RIFs) in the maritime sector. It makes important contributions in terms of a novel maritime accident database, new RIFs, findings, and implications. More specifically, the latest maritime accident data from 2017 to 2021 is collected from both the Global Integrated Shipping Information System (GISIS) and Lloyd’s Register Fairplay (LRF) databases. Based on the new dataset, 23 RIFs are identified, involving both dynamic and static risk factors. With these developments, new findings and implications are revealed beyond the state-of-the-art of maritime risk analysis. For instance, the research results show ship type, ship operation, voyage segment, deadweight, length, and power are among the most influencing factors. The new BN-based risk model offers reliable and accurate risk prediction results, evident by its prediction performance and scenario analysis. It provides valuable insights into the development of rational accident prevention measures that could well fit the increasing demands of maritime safety in today’s complex shipping environment.
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Ren, Xujie
29ba8411-40a4-45e8-808a-828b526f3243
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
1 November 2022
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Ren, Xujie
29ba8411-40a4-45e8-808a-828b526f3243
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
Li, Huanhuan, Ren, Xujie and Yang, Zaili
(2022)
Data-driven Bayesian network for risk analysis of global maritime accidents.
Reliability Engineering & System Safety, 230, [108938].
(doi:10.1016/j.ress.2022.108938).
Abstract
Maritime risk research often suffers from insufficient data for accurate prediction and analysis. This paper aims to conduct a new risk analysis by incorporating the latest maritime accident data into a Bayesian network (BN) model to analyze the key risk influential factors (RIFs) in the maritime sector. It makes important contributions in terms of a novel maritime accident database, new RIFs, findings, and implications. More specifically, the latest maritime accident data from 2017 to 2021 is collected from both the Global Integrated Shipping Information System (GISIS) and Lloyd’s Register Fairplay (LRF) databases. Based on the new dataset, 23 RIFs are identified, involving both dynamic and static risk factors. With these developments, new findings and implications are revealed beyond the state-of-the-art of maritime risk analysis. For instance, the research results show ship type, ship operation, voyage segment, deadweight, length, and power are among the most influencing factors. The new BN-based risk model offers reliable and accurate risk prediction results, evident by its prediction performance and scenario analysis. It provides valuable insights into the development of rational accident prevention measures that could well fit the increasing demands of maritime safety in today’s complex shipping environment.
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Accepted/In Press date: 25 October 2022
e-pub ahead of print date: 27 October 2022
Published date: 1 November 2022
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Local EPrints ID: 503671
URI: http://eprints.soton.ac.uk/id/eprint/503671
ISSN: 0951-8320
PURE UUID: 49e69395-f8e6-438c-9c48-8798a52c91bb
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Date deposited: 08 Aug 2025 16:41
Last modified: 22 Aug 2025 02:49
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Author:
Huanhuan Li
Author:
Xujie Ren
Author:
Zaili Yang
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