Data-driven Bayesian analysis of marine accidents in the English Channel

Abstract

The English Channel, renowned for its heavy traffic and challenging conditions, frequently witnesses marine accidents. To understand the causal mechanisms and identify risk factors, this study analyzed 833 accidents from 2017 to 2022, employing a Bayesian network predictive model. Validation using log loss, quadratic loss, and spherical gain metrics confirmed the model’s accuracy. The research elucidates the primary influences on accident occurrence: ship type, tonnage, accident location, and flag state. Ship-related factors surpass environmental ones in significance. Older ships face heightened risks of mechanical failures and collisions, especially in adverse weather with limited visibility. Yachts and fishing vessels are particularly susceptible to fire, explosion, collision, and grounding incidents. The study offers theoretical guidance for management departments to devise prevention strategies and enhance navigation safety in the English Channel.