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Analysis of factors affecting the severity of marine accidents using a data-driven Bayesian network

Cao, Y, Wang, X, Wang, Y, Fan, S, Wang, H, Yang, Z, Liu, Z, Wang, J and Shi, R (2023) Analysis of factors affecting the severity of marine accidents using a data-driven Bayesian network. Ocean Engineering, 269. ISSN 0029-8018

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A data-driven Bayesian network model (BN) is used to analyse the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). Firstly, based on the marine accident investigation reports involving 1,294 ships from 2000 to 2019, the severity grades of marine accidents are classified, and a database of factors affecting the severity of marine accidents is established. Secondly, a Tree Augmented Naive Bayesian algorithm (TAN) is used to establish a data-driven BN model, and the established database of AIFs is analysed by data training and machine learning to reveal the influence of related factors on the severity of the accident and the mechanism of action. Finally, the sensitivity analysis and verification of the model are conducted. Through the analysis of the Most Probable Explanation (MPE), it explains the possible configurations in different scenarios and identifies the related potential risks. This study finds that “accident type”, “engine power”, “gross tonnage”, “ship type” and “location” are the five most important AIFs of three accident severity grades. “Capsizing/sinking”, “hull/machinery damage” and “collision” that are most likely to lead to “very serious accidents”. Further, the possibility of fishing boats or other small ships leading to “very serious accidents” is also higher than that of other types of ships. The results of this study can help to analyse and predict marine accidents and ensure the safe navigation of ships and hence benefit such maritime stakeholders as safety authorities and ship owners.

Item Type: Article
Uncontrolled Keywords: 0405 Oceanography; 0905 Civil Engineering; 0911 Maritime Engineering; Civil Engineering
Subjects: G Geography. Anthropology. Recreation > GC Oceanography
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 24 Mar 2023 10:24
Last Modified: 03 Jan 2024 00:50
DOI or ID number: 10.1016/j.oceaneng.2022.113563
URI: https://researchonline.ljmu.ac.uk/id/eprint/19160
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