The Prediction of River Water Pollution Density Based on Data Mining Technology
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.
Zhenyu Du and X.B Sun
C. Y. Wang et al., "The Prediction of River Water Pollution Density Based on Data Mining Technology", Advanced Materials Research, Vols. 113-116, pp. 1285-1288, 2010