Multi-source data-driven Bayesian network for risk analysis of maritime accidents in the high sea

Abstract

Incomplete data significantly hampers risk analysis for high-sea maritime accidents (HSMAs). This paper introduces a novel multi-source data-driven Bayesian network (DDBN) framework to address this limitation. This framework initially integrates heterogeneous data from multiple sources, including accident reports, ship characteristics, environmental conditions, etc. The structural learning of the DDBN employs a hybrid, tri-source enhanced methodology. Informed by a two-stage risk evolution theory, this approach integrates evidence from structured data, text analysis, and expert knowledge to construct a unified network structure, ensuring that derived causal relationships align with both statistical evidence and domain expertise. Subsequently, the Expectation-Maximization algorithm is employed for parameter estimation to handle missing data. The findings indicate that although the accident type, sea area and ship type all contribute to the risk level of HSMAs, the gross tonnage is the most critical factor that directly affects the likelihood of an accident. The DDBN model achieves an accident prediction accuracy of 84.0%, with both precision and recall exceeding 75%. Furthermore, DDBN-based scenario analysis proactively identifies high-risk scenarios associated with specific accident types and gross tonnage, offering maritime authorities and operators an enhanced tool for risk assessment. This study provides a scientific basis for formulating targeted HSMA prevention strategies.