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Operational limits for aquaculture operations from a risk and safety perspective

机译：来自风险和安全视角的水产养殖业务的运行限制

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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 Naive 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.
机译：关于是否中止在挪威鱼类农场中的高风险水产养殖业务的决策主要是经验驱动的。现场人员决定是否基于其主观判断对他们是否能够处理这种情况来开始/延迟/中止运营。风险被视为隐含的“肠道感”。没有明确的操作限制，也没有结构化的过程，可以为高风险运营导出这些过程。在本研究中，已从主要事故方案中识别了预定义的安全关键属性，以指导机器学习过程以定义基于多源数据的操作限制。贝叶斯网络，树增强天真贝叶斯（TAN）搜索算法被选择以建立预测模型，以便可以决定给定条件的运行限制。本文得出结论，机器学习技术具有巨大的潜力，可用于支持高风险水产养殖业务的安全决策，并且基于风险的运营限制有助于更好地了解运营环境，并理解可能的差异可能的含义表明危险情况。

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