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

被引:1
作者
Park, Sang-A [1 ]
Park, Deuk-Jin [2 ]
Yim, Jeong-Bin [3 ]
Kim, Hyung-ju [4 ]
机构
[1] Pukyong Natl Univ, Grad Sch, Dept Fisheries Phys, Busan, South Korea
[2] Pukyong Natl Univ, Div Marine Prod Syst Management, 45 Yongso Ro, Busan 48513, South Korea
[3] Korea Maritime & Ocean Univ, Div Maritime AI & Cyber Secur, 727 Taejong Ro, Busan 49112, South Korea
[4] Norwegian Univ Sci & Technol, Trondheim, Norway
来源
MARITIME TRANSPORT RESEARCH | 2025年 / 8卷
基金
新加坡国家研究基金会;
关键词
Marine accident; Accident analysis; Bayesian network; Subject matter experts; Risk assessment; HUMAN ERROR; ACCIDENTS; SYSTEM;
D O I
10.1016/j.martra.2024.100128
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
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
引用
收藏
页数:19
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