The Prediction of River Water Pollution Density Based on Data Mining Technology

摘要

In order to increase the prediction precision, this article proposes a forecasting model in water pollution density based on data mining technology. The model consists of three stages: first, the rough set theory and the genetic algorithm are applied to select relevant forecasting variable to the water pollution density; second, training pattern of artificial neural network which is similar to the forecast term is carried out by using data mining technology; finally the artificial neural network is used to carry on forecasting the water pollution density. The applied result shows that this model has a higher precision and surpasses gray GM (1, 1) and the pure BP artificial neural network model.