Comparison and Analysis of Prediction Methods for Water Consumption per Ten Thousand Yuan Industrial Added Value

Zhi Jun Yan; Ming Yue Zhang; Chun Xiao Xu; Hai Tao Zhao; Yue Ping Tang; Hui Rong Wang

doi:10.4028/www.scientific.net/AMR.1073-1076.2653

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1073-1076Comparison and Analysis of Prediction Methods for...

Comparison and Analysis of Prediction Methods for Water Consumption per Ten Thousand Yuan Industrial Added Value

Abstract:

Water consumption per ten thousand yuan industrial added value (WCPIAV) is the assessment indicator to implement the most stringent water management system to control water efficiency. This paper proposes trend analysis method, elasticity coefficient analysis method and influencing factors analysis method to predict WCPIAV in Jiangsu province, the experimental areas where implement the most stringent water management system. The results show that different methods predict well in different cities, influencing factors analysis method works better than the other two methods. An appropriate method should be selected depending on the specific situation.

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Periodical:

Advanced Materials Research (Volumes 1073-1076)

Pages:

2653-2658

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1073-1076.2653

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Online since:

December 2014

Authors:

Zhi Jun Yan, Ming Yue Zhang*, Chun Xiao Xu, Hai Tao Zhao, Yue Ping Tang, Hui Rong Wang

Keywords:

Elastic Coefficient, Principal Component Analysis, Trend Fitting, Water Consumption per Ten Thousand Yuan Industrial Added Value

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* - Corresponding Author

References

[1] Tiejun Dai, Huiqiang Cheng: Industrial Water Treatment, 2008, 28(10): 9-12. (In Chinese).

Google Scholar

[2] Xiaping Wang: Journal of Economics of Water Resources, 2014, (2): 50-52. (In Chinese).

Google Scholar

[3] Xiaodong Gao, et al: Journal of Economics of Water Resources, 2013, (5): 1-5. (In Chinese).

Google Scholar

[4] Jiaping Xue, Lili Zhang: Shanxi Architecture, 2013, 39 (5): 199-201. (In Chinese).

Google Scholar

[5] Abdi H, Williams L J: Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459.

Google Scholar

[6] Shaw P J A. Multivariate statistics for the environmental sciences [M]. London: Arnold, (2003).

Google Scholar

[7] Yuezhen Han, Min Kong: Shanxi Architecture, 2007, 33(20): 297-298. (In Chinese).

Google Scholar