A hybrid risk assessment method combining CatBoost and FAHP-Grid search optimized risk matrix for container ship accident

Abstract

As a dominant mode of maritime transportation with unique risk characteristics, container shipping requires accurate and applicable risk assessment. However, conventional risk matrices oversimplify complex interactions, while pure data-driven models lack operational utility. To address this, a hybrid method for container ship risk assessment is proposed. This method integrates CatBoost-based predictive method, FAHP-grid search optimized risk matrix, and GIS-supported risk mapping. A comprehensive study of maritime casualties and piracy accidents is conducted, utilizing historical incident data sets collected from the Global Integrated Shipping Information System (GISIS). The global maritime accident risk of container ships is then evaluated and mapped. The sensitivity analysis confirms the robustness of the method under varying linguistic distance parameters, while expert weights have a moderate impact on the assessment results. Finally, the effectiveness of the proposed method is validated through comparative analyses on predictive performance, risk discrimination capability, and risk assessment accuracy. CatBoost algorithm outperforms XGBoost, LightGBM, and Random Forest algorithms in predictive metrics. The designed risk matrix shows strong discriminatory ability for container ship risk levels. In historical accident data validation, the proposed method also achieves higher accuracy than combinations involving XGBoost, LightGBM, or Random Forest with the designed risk matrix.