On the causation correlation of maritime accidents based on data mining techniques

Xiaoxue Ma, He Lan, Weiliang Qiao, Bing Han and Heilong He

Journal of Risk and Reliability, 2024, vol. 238, issue 5, 905-919

Abstract: A great deal of valuable information included in maritime accident reports needs to be excavated to contribute to accident prevention or risk defence. In the present study, data mining technologies are applied to explore the potential causation correlations among the risk factors associated with maritime accidents. A collection of 285 accident reports is subjected to database analysis by using text mining technology to extract keywords, and the critical factors are then determined with reference to objective reports. The FP-Growth (frequent pattern growth) algorithm is then applied to identify the association rules hidden in the causations leading accidents, and the strength level of the identified association rules is evaluated quantitatively. The results show that the data mining technologies are applicable for identifying correlations hidden among factors contributing to maritime accidents. In addition, single factors do not significantly lead to accidents, while the integration of factors can easily cause accidents even under the condition of a good navigation environment. Therefore, stakeholders involved in maritime activities are advised to systematically assess risk factors, and prevent maritime accidents by interrupting the correlations among the factors.

Keywords: Maritime accidents; risk analysis; text mining; association rule; FP-Growth algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:238:y:2024:i:5:p:905-919

DOI: 10.1177/1748006X221131717

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