Feng, Y, Wang, H, Xia, G, Cao, W, Li, T, Wang, X and Liu, Z (2024) A machine learning-based data-driven method for risk analysis of marine accidents. Journal of Marine Engineering & Technology, 24 (2). pp. 147-158. ISSN 2046-4177

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Abstract

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods.

Item Type:	Article
Additional Information:	This is an Accepted Manuscript version of the following article, accepted for publication in Journal of Marine Engineering & Technology. Feng, Y., Wang, H., Xia, G., Cao, W., Li, T., Wang, X., & Liu, Z. (2024). A machine learning-based data-driven method for risk analysis of marine accidents. Journal of Marine Engineering & Technology, 24(2), 147–158. https://doi.org/10.1080/20464177.2024.2368914. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (Deed - Attribution-NonCommercial-NoDerivatives 4.0 International - Creative Commons ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Uncontrolled Keywords:	Maritime safety; Marine accident; Machine learning; Association rule mining; Complex network; 40 Engineering; 46 Information and Computing Sciences; 4007 Control Engineering, Mechatronics and Robotics; 4602 Artificial Intelligence; 4015 Maritime Engineering; Machine Learning and Artificial Intelligence; 4007 Control engineering, mechatronics and robotics; 4015 Maritime engineering; 4602 Artificial intelligence
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering
Publisher:	Taylor and Francis Group
SWORD Depositor:	A Symplectic
Date Deposited:	02 Apr 2025 09:47
Last Modified:	02 Apr 2025 09:47
DOI or ID number:	10.1080/20464177.2024.2368914
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26063

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