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

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 113-116)

Edited by:

Zhenyu Du and X.B Sun

Pages:

1285-1288

DOI:

10.4028/www.scientific.net/AMR.113-116.1285

Citation:

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

Online since:

June 2010

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Price:

$35.00

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