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.
Highlights
The best success rate reaches 87.4% with ROC area 0.942, which means 87.4% of the cases can be appropriately classified based on the pre-identified attributes. This can be considered as a significant result in the domain of aquaculture operation, in which we don't have all the possible information at our disposal
The training dataset is used to build the pre diction model, while the test dataset is to estimate the accuracy of the classification model
The Bayesian network Tree Augmented Naïve Bayes (TAN) learning algorithm treats the classification node as the first node in the ordering to learn the structure, which means the classification node is treated as the parent of all other nodes
Summary
The Norwegian fish farming industry is expected to grow fivefold by 2050 [1] compared to 2010. The most recent figures from Statistics Norway show that in 2018, the sector pro duced 1.35 million tons of fish for human consumption, with a first-hand value of almost €6.5 billion, of which, Atlantic salmon made up 95% of the total [2]. The industry is facing challenges of a lack of sheltered coastal sites and increasing negative ecological consequences due to sea lice, fish escapes and farm waste left on the seabed [3]. The industry is experiencing technological innovations in more exposed locations. It is especially challenging to get skilled staff at exposed locations [6]
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