Machine learning in management of precautionary closures caused by lipophilic biotoxins

Highlights

• ML Models for HAB prediction and shellfish farming management decision support.

• Study of the importance of variables involved in the forecasting of HAB.

• Analysing the behaviour of machine learning models against unbalanced datasets.

• Prediction of production cessation by HAB with 91.83% accuracy and 97.34% recall.

Abstract

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.