Machine Learning in management of precautionary closures caused by
lipophilic biotoxins
- URL: http://arxiv.org/abs/2402.09266v1
- Date: Wed, 14 Feb 2024 15:51:58 GMT
- Title: Machine Learning in management of precautionary closures caused by
lipophilic biotoxins
- Authors: Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos and
Daniel Rivero
- Abstract summary: 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.
This work proposes a predictive model capable of supporting the application of precautionary closures.
- Score: 43.51581973358462
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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