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Application of Artificial Neural Networks for the Monitoring of Episodes of High Toxicity by DSP in Mussel Production Areas in Galicia
- Source :
- RUC. Repositorio da Universidade da Coruña, instname, Proceedings, Volume 54, Issue 1, RUC: Repositorio da Universidade da Coruña, Universidade da Coruña (UDC), Proceedings, Vol 54, Iss 12, p 12 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- This study seeks to support, through the use of Artificial Neural Networks (ANN), the decision to perform closings after days without sampling in the Vigo estuary. The opening and closing of the mussel production areas are based on the toxicity analysis of this bivalve&rsquo<br />s meat. Sometimes it is not possible to obtain the necessary data for effective closing. If there is evidence of an increase in toxicity levels, &ldquo<br />Precautionary Closings&rdquo<br />on mussel extraction is done. A small error in the forecast of the state of the areas could mean serious losses for the mussel industry and a huge risk for public health. Unlike in previous studies, this study aims to manage the state of the mussel production areas, whilst the others focused on predicting the harmful algae blooms. Having achieved test sensitivity values of 67.40% and test accuracy of 83.00%, these results may lead to new research that involves obtaining more accurate models that can be integrated into a support system.
- Subjects :
- harmful algae blooms
Artificial neural network
Artificial neural networks
Harmful algae blooms
Sampling (statistics)
lcsh:A
Test sensitivity
Mussel
Algal bloom
Fishery
machine learning
Machine learning
Environmental science
Production (economics)
Support system
lcsh:General Works
artificial neural networks
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- RUC. Repositorio da Universidade da Coruña, instname, Proceedings, Volume 54, Issue 1, RUC: Repositorio da Universidade da Coruña, Universidade da Coruña (UDC), Proceedings, Vol 54, Iss 12, p 12 (2020)
- Accession number :
- edsair.doi.dedup.....bf48ab83f86f81dc919707bba341f69e