An Improved Gray Model For Aquaculture Water Quality Prediction

Abstract Water quality management is one of the key problems for intensive aquaculture. In order to predict the aquaculture water quality accurately, the paper proposes an Improved Gray Model for aquaculture water quality prediction. The Gray Model is improved and combined with the BP Neural Networks to implement the prediction. The Improved Gray Model serves as the main body for prediction and BP Neural Networks are used to judge the prediction results. Experiments using the temperature and DO data collected from the aquatic factories of Yixing, Dongying in China proved that the prediction accuracy improved 15% than the traditional prediction method.

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