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首页> 外文期刊>Proceedings >Application of Artificial Neural Networks for the Monitoring of Episodes of High Toxicity by DSP in Mussel Production Areas in Galicia
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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

机译:人工神经网络在加利西亚贻贝生产区贻贝生产区中的高毒性剧集监测

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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’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, “Precautionary Closings” 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.
机译:本研究旨在通过使用人工神经网络(ANN)来支持,决定在没有对Vigo河口中抽样的情况下进行关闭的。贻贝生产区的开放和关闭是基于这种双抗体肉的毒性分析。有时无法获得有效关闭的必要数据。如果有证据表明毒性水平的增加,则进行贻贝提取的“预防闭合”。该地区国家预测中的一个小错误可能意味着贻贝工业的严重损失以及公共卫生的巨额风险。与以往的研究不同,本研究旨在管理贻贝生产领域的状态,而其他人则专注于预测有害藻类绽放。实现测试敏感值67.40%,测试精度为83.00%,这些结果可能导致新的研究,涉及获得可以集成到支持系统中的更准确的模型。

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