A Bayesian network model integrating data and expert insights for fishing ship risk assessment

Le résumé fourni par la source

• A bayesian network (FABN) was developed by integrating fishing ship accident data and expert insights, enabling dynamic assessment of accident probabilities and identification of critical risk factors. • A FABN scenario was modeled by combining objective data on marine accidents and SME data. • Sensitivity analysis revealed high-impact factors, such as "Loss of Stability" for capsizing and "Pipe Breakage" for flooding, while statistical validation confirmed the robustness of the FABN model through chi-square testing. • The FABN model offers actionable insights for implementing RCOs, such as stricter loading regulations and enhanced maintenance protocols, reducing accident probabilities in fishing ship operations. • FABN outputs are applicable to existing SMS, supporting probabilistic risk evaluation, "what-if" scenario simulations, and dynamic decision-making, enhancing overall fishing ship safety. Marine accidents can result in severe economic losses and casualties, underscoring the critical need for effective risk assessment.. In this study, quantitative marine accident reports from Korea that objectively describe accident variables were collected and classified to analyze marine accidents of fishing ships To analyze the causes of accidents involving different types of fishing ships, a survey with subject matter experts (SMEs) was conducted. A fishing ship accident Bayesian network (FABN) scenario was then developed by integrating fishing ship accident data with SME insights. The FABN was comprehensively modeled based on the scenario, with marine accidents being modeled based on causal variables each marine accident. Changes in the output value of the FABN were verified via a sensitivity analysis, and the independence and statistical significance of the model were confirmed using a statistical analysis of the collected data. FABN allows for the immediate assessment of the probability of marine accidents related to fishing ships by utilizing network structures, and provides the advantage of structurally assessing ship accident risks

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