Risk analysis of man overboard scenario in a small fishing vessel

Abstract One major accident scenario aboard fishing vessels is “man overboard” (MOB). Prevention of this accident scenario would reduce the high fatality rate in the fishing industry. Critical understanding of the risk factors is vital for a robust risk assessment of this accident scenario and to develop interventions. This paper presents the Objected-Oriented Bayesian Network (OOBN) application for risk assessment of the MOB scenario. The OOBN model is developed to probabilistically capture the key accident influencing factors in fragmented structures. The proposed methodology is demonstrated in an accident scenario, and the model captures the dynamic dependencies and interdependencies among basic variables and establishes their degree of influence on the accident occurrence probability. The vulnerability path was identified, and a pre-and post-accident intervention plan was proposed to minimize the accident occurrence and its associated risk. Applying the methodology provides vital safety-based information that could be adopted for small vessel operation and maritime administration regulation.