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Water Demand Forecasting by Characteristics of City Using Principal Component and Cluster Analyses

  • Choi, Tae-Ho (Department of Environmental Engineering, University of Seoul) ;
  • Kwon, O-Eun (Korean Intellectual Property Office) ;
  • Koo, Ja-Yong (Department of Environmental Engineering, University of Seoul)
  • Received : 2009.11.20
  • Accepted : 2010.08.03
  • Published : 2010.09.30

Abstract

With the various urban characteristics of each city, the existing water demand prediction, which uses average liter per capita day, cannot be used to achieve an accurate prediction as it fails to consider several variables. Thus, this study considered social and industrial factors of 164 local cities, in addition to population and other directly influential factors, and used main substance and cluster analyses to develop a more efficient water demand prediction model that considers unique localities of each city. After clustering, a multiple regression model was developed that proved that the $R^2$ value of the inclusive multiple regression model was 0.59; whereas, those of Clusters A and B were 0.62 and 0.74, respectively. Thus, the multiple regression model was considered more reasonable and valid than the inclusive multiple regression model. In summary, the water demand prediction model using principal component and cluster analyses as the standards to classify localities has a better modification coefficient than that of the inclusive multiple regression model, which does not consider localities.

Keywords

References

  1. Shim MH. Estimating long-term water demand by principal component and cluster analysis in central Seoul [dissertation]. Seoul: University of Seoul; 2005.
  2. Arbues F, Garcia-Valinas MA, Martinez-Espineira R. Estimation of residential water demand: a state-of-the-art review. J. Soc. Econ. 2003;32:81-102. https://doi.org/10.1016/S1053-5357(03)00005-2
  3. Kim YS. A study on estimation of domestic water demand by actual survey data [dissertation]. Seoul: University of Seoul; 2007.
  4. Reynaud A. An econometric estimation of industrial water demand in France. Environ. Resource Econ. 2003;25:213-232. https://doi.org/10.1023/A:1023992322236
  5. Pyon SS. Long-term water demand forecasting using the system dynamics model [dissertation]. Seoul: University of Seoul; 2003.
  6. Billings RB, Jones CV. Forecasting urban water demand. Denver: American Water Works Association; 1996.
  7. Forrester JW. System dynamics as an organizing framework for pre-coiiege education. Syst. Dynam. Rev. 1993;9:183-194. https://doi.org/10.1002/sdr.4260090207
  8. Morell I, Gimenez E, Esteller MV. Application of principal components analysis to the study of salinization on the Castellon Plain (Spain). Sci. Total Environ. 1996;177:161-171. https://doi.org/10.1016/0048-9697(95)04893-6
  9. Grove DM, Roberts CA. Principal component and cluster analysis of 185 large towns in England and Wales. Urban Stud. 1980;17:77-82. https://doi.org/10.1080/00420988020080091
  10. Cochran R, Cotton AW. Municipal water demand study, Oklahoma City and Tulsa, Oklahoma. Water Resour. Res. 1985;21:941-943. https://doi.org/10.1029/WR021i007p00941

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