- Volume 15 Issue 3
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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
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
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