A big data-driven meta classifier for cross-sectional classification of vessel incident risk

Major vessel incidents, such as collisions, groundings, and oil spills, impose significant financial losses on shipping stakeholders. To assess the risk of future incidents, this study proposes a big data-driven classification approach to identify incident-prone vessels. Based on a comprehensive global vessel intelligence dataset (2017–2022), we develop four base machine learning classifiers and a meta classifier with hybrid feature selection. The meta classifier validated on out-of-sample data in year 2022, demonstrates superior classification precision and sorting ability, outperforming state-of-the-art models and an industry-adopted vehicle incident risk assessment method. The proposed solution provides an effective tool for stakeholders to evaluate vessel incident risk levels, aiding decisions on vessel purchases, charters, marine insurance premium design, and risk control. By offering a practical and scalable …

Recommended citation: Y Cai, T Chen, M Liang, Q Meng. (2025). A big data-driven meta classifier for cross-sectional classification of vessel incident risk. Ocean Engineering, 342, 123058. https://www.sciencedirect.com/science/article/pii/S0029801825027416