A Bayesian network model integrating data and expert insights for fishing ship risk assessment
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 o...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Maritime Transport Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666822X24000261 |
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| author | Sang-A Park Deuk-Jin Park Jeong-Bin Yim Hyung-ju Kim |
| author_facet | Sang-A Park Deuk-Jin Park Jeong-Bin Yim Hyung-ju Kim |
| author_sort | Sang-A Park |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-67b53b32e0fa4956b6af63d7710172a8 |
| institution | Kabale University |
| issn | 2666-822X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Maritime Transport Research |
| spelling | doaj-art-67b53b32e0fa4956b6af63d7710172a82025-01-14T04:12:39ZengElsevierMaritime Transport Research2666-822X2025-06-018100128A Bayesian network model integrating data and expert insights for fishing ship risk assessmentSang-A Park0Deuk-Jin Park1Jeong-Bin Yim2Hyung-ju Kim3Department of Fisheries Physics, The Graduate School, Pukyung National University, Republic of KoreaDivision of Marine Production System Management, Pukyong National University, 45 Yongso-Ro, Nam-Gu, Busan 48513, Republic of Korea; Corresponding author.Division of Maritime AI & Cyber Security, Korea Maritime and Ocean University, 727 Taejong-Ro, Yeongdo-Gu, Busan 49112, Republic of KoreaNorwegian University of Science and Technology, Located in Trondheim, NorwayMarine 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 riskshttp://www.sciencedirect.com/science/article/pii/S2666822X24000261Marine accidentAccident analysisBayesian networkSubject matter expertsRisk assessment |
| spellingShingle | Sang-A Park Deuk-Jin Park Jeong-Bin Yim Hyung-ju Kim A Bayesian network model integrating data and expert insights for fishing ship risk assessment Maritime Transport Research Marine accident Accident analysis Bayesian network Subject matter experts Risk assessment |
| title | A Bayesian network model integrating data and expert insights for fishing ship risk assessment |
| title_full | A Bayesian network model integrating data and expert insights for fishing ship risk assessment |
| title_fullStr | A Bayesian network model integrating data and expert insights for fishing ship risk assessment |
| title_full_unstemmed | A Bayesian network model integrating data and expert insights for fishing ship risk assessment |
| title_short | A Bayesian network model integrating data and expert insights for fishing ship risk assessment |
| title_sort | bayesian network model integrating data and expert insights for fishing ship risk assessment |
| topic | Marine accident Accident analysis Bayesian network Subject matter experts Risk assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2666822X24000261 |
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