Application of Artificial Neural Networks for the Monitoring of Episodes of High Toxicity by DSP in Mussel Production Areas in Galicia
| Autor: | Enrique Fernandez-Blanco, Daniel Rivero, Andrés Molares |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
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 |
| Zdroj: | 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) |
| Popis: | 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 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 Precautionary Closings&rdquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|