Prediction of the severity of marine accidents using improved machine learning

Yinwei Feng, Xinjian Wang, Qilei Chen, Zaili Yang, Jin Wang, Huanhuan Li, Guoqing Xia and Zhengjiang Liu

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 188, issue C

Abstract: Although many studies have focused on the occurrence likelihood of marine accidents, few have focused on the analysis of the severity of the consequences, and even fewer on the prediction of the severity. To this end, a new research framework is proposed in this study to accurately predict the severity of marine accidents. First, a novel two-stage feature selection (FS) method was developed to select and rank Risk Influential Factors (RIFs) to improve the accuracy of the Machine Learning (ML) model and interpretability of the FS. Second, a comprehensive evaluation method is proposed to measure the performance of the FS methods based on stability, predictive performance improvement, and statistical tests. Third, six well-established ML models were used and compared to measure the performance of different predictors. The Light Gradient Boosting Machine (LightGBM) was found to have the best predictive performance for the severity prediction of marine accidents and was treated as the benchmark model. Finally, LightGBM was used to predict accident severity based on the RIFs selected by the proposed FS method, and the effect of risk control measures was counterfactually analysed from a quantitative perspective. This innovative study on the use of improved ML approaches can effectively analyse and predict the severity of marine accidents, providing a novel methodology for and triggering a new direction for using Artificial Intelligence (AI) technologies in safety assessment and accident prevention studies. The source code is publicly available at: https://github.com/FengYinLeo/PGI-SDMI.

Keywords: Maritime transportation; Marine accident; Machine learning; Mutual information; Feature selection; LightGBM (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103647

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