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

Chen Ye Wang; Bin Sheng Liu; Er Wei Qiu

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

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 113-116The Prediction of River Water Pollution Density...

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.

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Periodical:

Advanced Materials Research (Volumes 113-116)

Pages:

1285-1288

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.113-116.1285

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Online since:

June 2010

Authors:

Chen Ye Wang, Bin Sheng Liu, Er Wei Qiu

Keywords:

BP Neural Network (BPNN), Data Mining Technology, Prediction, Water Pollution Density

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References

[1] Theresa Beaubouef, Roy Ladner, Frederick Petry. Rough Set Spatial Data Modeling for Data Mining. International Journal of Intelligent Systems. Vol 19(2004): 567-584.

DOI: 10.1002/int.20019

Google Scholar

[2] Liang Qi-bin. Prediction of the Dianchi Lake Water Pollution by Grey System GM(1, 1). Environmental Science Survey. Vol 4(2004): 11-17.

Google Scholar

[3] Zhou Jian-kang. Application of BP neural network based on MATLAB in prediction of river water pollution density. Water Resources and Hydropower Engineering. Vol 9(2003): 21-26.

Google Scholar