A new water quality assessment model based on projection pursuit technique

被引：39

作者：

ZHANG, Chi ^{[1
]}

DONG, Sihui ^{[2
]}

机构：

[1] School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian

[2] School of Environment Science and Engineering, Dalian Jiaotong University, Dalian

来源：

Journal of Environmental Sciences | 2009年 / 21卷 / SUPPL. 1期

基金：

中国国家自然科学基金;

关键词：

assessment model; genetic algorithm; projection pursuit; water quality;

D O I：

10.1016/S1001-0742(09)60062-0

中图分类号：

学科分类号：

摘要：

A new water quality assessment model was built based on projection pursuit technique. A great quantity of sample data was applied to increase the model's precision. A new genetic algorithm combined with conditional optimization method was proposed and applied to the model optimization, which could deal with global optimization problem with various restrictions effectively. The case study shows that this model can give an appropriate assessment of water quality. Moreover, it can determine the index weights in an objective way or provide information for decision makers, which is difficult for other assessment methods. © 2009 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences.

引用

页码：S154 / S157

页数：3

共 10 条

[1]

Bobbie J.M., Webb R., Kristin H.J., Scott D.H., Christian P., Bob W.W., An improved optimization algorithm and a bayes factor termination criterion for sequential projection pursuit, Chemometrics and Intelligence Laboratory Systems, 77, 1-2, pp. 149-160, (2005)

[2]

Chipperfield A.J., Fleming P.J., The MATLAB genetic algorithm toolbox, applied control techniques using MATLAB, IEE Colloquium, (1995)

[3]

Friedman J.H., Turkey J., Projection pursuit algorithm for exploratory data analysis, IEEE Trans on Computer, 19, 4, pp. 224-227, (1974)

[4]

Ifarragaerri A., Chang C.I., Unsupervised hyperspectral image analysis with projection pursuit, IEEE Trans on Geoscience and Remote Sensing, 38, 6, pp. 2529-2538, (2000)

[5]

Jiang B.Q., Wang W.S., Wen X.C., An improved BP neural networks model on water quality evaluation, Computer Systems Applications, 9, pp. 46-50, (2007)

[6]

Jin J.L., Huang H.M., Wei Y.M., Comprehensive evaluation model for water quality based on combined weights, Journal of Hydroelectric Engineering, 23, 3, pp. 13-19, (2004)

[7]

Luo D.G., Wang X.J., Guo Q., The application of ANN realized by MATLAB to underground water quality assessment, Acta Scicentiarum Naturalum Universitis Pekinesis, 40, 2, pp. 296-302, (2004)

[8]

Michael D.S., Statistical Modeling of High-Dimensional Nonlinear Systems: A Projection Pursuit Solution, pp. 13-88, (2005)

[9]

Tian J.H., Qiu L., Chai F.X., Application of fuzzy recognition in comprehensive evaluation of water quality, Acta Scientiae Circumstantiae, 25, 7, pp. 950-953, (2005)

[10]

Zhang X.L., Ding J., Li Z.Y., Application of new projection pursuit algorithm in assessing water quality, China Environ mental Science, 20, 2, pp. 187-189, (2000)

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