Hybrid analysis of causal factors of marine accidents in ports

Marine accidents in port areas pose significant risks due to increasing vessel traffic and operational complexity. These incidents can cause economic losses, environmental damage, and serious consequences for human life. Therefore, analysing their causes and developing preventive strategies is essential. This study proposes a hybrid approach integrating the Human Factors Analysis and Classification System (HFACS) with Bayesian Networks (BN) to examine the causes of port accidents. Based on the analysis of 150 accident reports, the model classifies human errors and organizational deficiencies and probabilistically models their causal relationships. Findings indicate that decision-based errors, rule violations, authority abuses, and procedural deficiencies are critical factors, while unsafe operational conditions frequently trigger accidents in port operations. Unlike previous studies in aviation and maritime domains, this research extends the HFACS framework into a five-level structure reflecting the high-density, spatially constrained, and multi-stakeholder nature of port environments. The additional layer represents operational constraints in port waters as a distinct causal level. Conditional probability tables are developed using a Fuzzy-SAM-based expert elicitation approach rather than conventional frequency-based estimation or simple expert averaging. This method models expert consensus under uncertainty and strengthens BN parameterization. The proposed framework supports understanding port accident causation and developing effective preventive strategies.