Study on Improved PCA-SVM Model for Water Demand Prediction

Xiang Hong Xue; Xiao Feng Xue; Lei Xu

doi:10.4028/www.scientific.net/AMR.591-593.1320

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 591-593Study on Improved PCA-SVM Model for Water Demand...

Study on Improved PCA-SVM Model for Water Demand Prediction

Abstract:

construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this model is higher than SVM, BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.

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Periodical:

Advanced Materials Research (Volumes 591-593)

Pages:

1320-1324

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.591-593.1320

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Online since:

November 2012

Authors:

Xiang Hong Xue, Xiao Feng Xue, Lei Xu

Keywords:

Genetic Algorithm (GA), Principal Component Analysis (PCA), Support Vector Machine (SVM), Water Demand

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References

[1] J.P. Liu and M.Q. Chang: Journal of China Hydrology, Vol.27(2007)No.5,p.11.(In Chinese)

Google Scholar

[2] J.F. Adamowski:Journal of Water Resources Planning and Management, Vol.134(2008) No.2, P.119.

Google Scholar

[3] J.P. Liu and M.Q. Chang: Journal of Taiyuan University of Technology, Vol.39(2008) No.3, p.299. (In Chinese)

Google Scholar

[4] S.Y.Lu, Y.S.Xu,Y.Xiong and Y.D. Mei: Engineering Journal of Wuhan University, Vol.44 (2011) No.5, p.565.

Google Scholar

[5] Y. Zhang and T.F. Yin: Computer Simulation,Vol.28(2011)No.9,p.357. (In Chinese)

Google Scholar

[6] X.J. Long, J.Qian and C.Liang:Journal of Chengdu University of Technology(Science & Technology Edition),Vol.37(2010)No.2,p.206. (In Chinese)

Google Scholar

[7] S.Liu and Y.Y Li: Journal of Harbin Engineering University,Vol.28(2007)No.4,p.398.

Google Scholar

[8] H.Y. Chen Y.G. Teng and J.S. Wang: Journal of Ecology and Rural Environment, Vol.26(2010) No.6, p.600. (In Chinese)

Google Scholar

[9] C.W.Hu,C.C. Chang and C.J. Lin:http://www.csie.ntu.edu.tw/cjlin/ papers/ guide/ guide. Pdf

Google Scholar