Paper Title:
The Prediction of River Water Pollution Density Based on Data Mining Technology
  Abstract

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, B. S. Liu, E. W. Qiu, "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
$32.00
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