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Operational limits for aquaculture operations from a risk and safety perspective

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  • Yang, Xue
  • Ramezani, Ramin
  • Utne, Ingrid Bouwer
  • Mosleh, Ali
  • Lader, PÃ¥l Furset

Abstract

Current decision making regarding whether to abort a high-risk aquaculture operation in a Norwegian fish farm is mainly experience-driven. The on-site personnel decides whether to start/delay/abort operations primarily based on their subjective judgement about whether they can handle the situation. The risk is considered implicitly as “gut feelings†. There are no explicit operational limits nor a structured process to derive these for high-risk operations. In this research, a predefine safety-critical attributes have been identified from major accident scenarios to guide machine learning process to define operational limits based on multi-source data. Bayesian network, Tree Augmented Naïve Bayes (TAN) search algorithms were selected to build up prediction model so that operational limits upon a given condition can be decided. The paper concludes that machine learning techniques have great potential to be used to support safe decision-making in high-risk aquaculture operation, and the risk-based operational limits facilitates better understanding of operational context, and comprehension of the meaning of several deviations which may indicate a dangerous situation.

Suggested Citation

  • Yang, Xue & Ramezani, Ramin & Utne, Ingrid Bouwer & Mosleh, Ali & Lader, PÃ¥l Furset, 2020. "Operational limits for aquaculture operations from a risk and safety perspective," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020307092
    DOI: 10.1016/j.ress.2020.107208
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    References listed on IDEAS

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    Cited by:

    1. Orestis Stavrakidis-Zachou & Konstadia Lika & Panagiotis Anastasiadis & Nikos Papandroulakis, 2021. "Projecting climate change impacts on Mediterranean finfish production: a case study in Greece," Climatic Change, Springer, vol. 165(3), pages 1-18, April.
    2. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Panagiotis K. Marhavilas & Fani Pliaki & Dimitrios Koulouriotis, 2022. "International Management System Standards Related to Occupational Safety and Health: An Updated Literature Survey," Sustainability, MDPI, vol. 14(20), pages 1-29, October.

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