Modeling on Sensitivity of Influential Factors to City Water Demand Based on System Dynamic Mechanics Method

  • Guang-Hui Wei
  • Feng Liu
  • Liang Ma
  • Liang-Liang Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)


To study the sensitivity of the influential factors to city water demand, thus promoting the construction of water-saving society. The paper gives a preliminary qualitative analysis by applying system dynamic mechanics method and gray relational analysis in city water demand, followed by the default factor analysis and principal component analysis further authentication. The results showed that: city population and GDP per capita are more sensitive than any other factors to the city water demand; results of system dynamic mechanics method and gray relational analysis method is basically same as principal component analysis model based on the default factor method; the six factors have multi-co linearity on city water demand, the city water demand prediction model based on principal component regression method can make variable reasonable, in line with the objective interpretation of the actual physical cause. This provides a new thinking and methods for government developing water-saving policy and water resources planning.


System dynamic mechanics City water demand Sensitive factor Gray relational analysis Principal component analysis 



This research work was supported by Xinjiang Water Resources and Hydropower Engineering Key Discipline Funding (award number. XJZDXK2002-10-05) and Xinjiang Hydrology Water Resources Key Discipline Funding (award number.XJSWSZYZDXK2010-12-02). We give the most heartfelt thanks to the reviewers for their great contributions on the early manuscript. We are also grateful to the editor who has helped improve the present paper with their most appropriate suggestions.


  1. 1.
    Wei G-H, Ma L (2009) Prediction of water surface evaporation based on grey relation analysis and RBF neural network. Arid Meteorol 27(1):73–77Google Scholar
  2. 2.
    Deng J-L (1987) Gray system basic approach, vol 3, issue 3. Huazhong University Press, Wuhan, pp 34–40Google Scholar
  3. 3.
    Wu L (2002) Bureau of statistics of Aksu prefecture. Aksu Stat Yearb 4(3):30–34Google Scholar
  4. 4.
    Wu L (2005) Bureau of statistics of Aksu prefecture. Aksu Stat Yearb 4(3):12–14Google Scholar
  5. 5.
    Liu S, Xie N (2008) Grey system theory and its applications, vol 4, issue 2, 4th edn. Science Press, Beijing, pp 11–13Google Scholar
  6. 6.
    Chen H, Feng L, Lina S (2010) Analysis on Nanjing water resources carrying capacity based on principal component. J Yangtze River 41(12):95–98Google Scholar
  7. 7.
    Yu G, Li Z, Zhang X et al (2009) Dynamic simulation of soil water-salt using BP neural network model and grey correlation analysis. Trans CSAE 25(11):74–79Google Scholar
  8. 8.
    Liu G (2009) Cause of multi-collinearity and its diagnosis treatment. Hefei Univ Technol Nat Sci 24(4):607–610Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guang-Hui Wei
    • 1
  • Feng Liu
    • 1
  • Liang Ma
    • 1
  • Liang-Liang Chen
    • 1
  1. 1.School of Water Resources and Civil EngineeringXinjiang Agricultural UniversityUrumqiChina

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