Machine Learning in management of precautionary closures caused by lipophilic biotoxins
This work addresses the risk of harmful algal blooms for mussel farmers in Galicia, Spain, by providing a decision-support tool, though it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of managing precautionary closures in mussel farming due to harmful algal blooms by developing a predictive model using the kNN algorithm, achieving sensitivity of 97.34%, accuracy of 91.83%, and a kappa index of 0.75.
Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of a confirmatory sampling and the existence of risk factors, precautionary closures may be applied. These decisions are made by experts without the support or formalisation of the experience on which they are based. Therefore, this work proposes a predictive model capable of supporting the application of precautionary closures. Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and 0.75 respectively, the kNN algorithm has provided the best results. This allows the creation of a system capable of helping in complex situations where forecast errors are more common.