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Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

Author

Listed:
  • Seung-Hyun Lee

    (Training Ship, Pukyong National University, Busan 48513, Republic of Korea)

  • Su-Hyung Kim

    (Training Ship, Pukyong National University, Busan 48513, Republic of Korea)

  • Kyung-Jin Ryu

    (Division of Marine Production System Management, Pukyong National University, Busan 48513, Republic of Korea)

  • Yoo-Won Lee

    (Division of Marine Production System Management, Pukyong National University, Busan 48513, Republic of Korea)

Abstract

The Marine Stewardship Council estimates that approximately 38 million people worldwide work in fisheries, and more than one-third of the global population is dependent on aquatic products for protein, highlighting the importance of sustainable fisheries. The FISH Safety Foundation reports that 300 fishers die every day. To achieve sustainable fisheries as a primary industry, the safety of human resources is of the utmost importance. The International Maritime Organization (IMO) and the International Labor Organization (ILO) have made efforts towards this goal, including the issuance of agreements and guidelines to reduce industrial accidents among fishing vessel workers. The criterion for applying these guidelines is usually a total ship length ≥12 m or ≥24 m. However, a vast majority of registered fishing vessels are <12 m long, and the fishers of these vessels suffer substantially more industrial accidents. Thus, we conducted a quantitative analysis of 1093 industrial accidents affecting fishers on fishing vessels <12 m in length, analyzed risk using a Bayesian network analysis (a method proposed by the Formal Safety Assessment of the IMO), and administered a questionnaire survey to a panel of experts in order to ascertain the risk for different types of industrial accidents and propose specific measures to reduce this risk.

Suggested Citation

  • Seung-Hyun Lee & Su-Hyung Kim & Kyung-Jin Ryu & Yoo-Won Lee, 2024. "Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length," Sustainability, MDPI, vol. 16(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:3977-:d:1391457
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    References listed on IDEAS

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    1. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    3. Fang Wang & Weijie Du & Hongxiang Feng & Yun Ye & Manel Grifoll & Guiyun Liu & Pengjun Zheng, 2023. "Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
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