Estimating regional water demand in Seoul, South Korea, using principal component and cluster analysis

J. Y. Koo, M. J. Yu, S. G. Kim, M. H. Shim, A. Koizumi

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In Seoul, the multiple regression models were used to estimate future water demand and to verify the ability of the water supply to cope with regional development. A regional development project extending over two districts was planned to stimulate the regional economy of Seoul in October 2003, and multiple regression models for each district were developed to verify the capacity of water facilities and the retention time of reservoirs. Two variables, the population and the area of the commercial district, were used to express domestic and commercial water usage. Coefficients for variables of models should be positive values; however, the coefficient for population was negative in Jung-gu. The prediction of water demand with one regression formula for each district may not be sufficient to characterize the water use pattern of a district. So, by characterizing each sub-district of the two districts, applying principal component and cluster analysis, they were divided into residential and commercial groups. Then, multiple regression models with the same variables were developed for each group. As a result, the models not only had positive coefficients for all variables, but also could provide reasonable sensitivity for the variables. For each group, the commercial area had nearly same sensitivity, but the population in the commercial area showed more sensitivity than in the residential area, because people living in the commercial area do not have to go to another district to work or sleep. Future water demands were estimated, depending on three scenarios of regional development, using the existing and newly developed models. The water demands estimated by the newly developed model are 3,416-11,372 ton/day less than those by existing model. Therefore, the model developed gave the correct water demand and prevented a wrong decision from being made.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalWater Science and Technology: Water Supply
Issue number1
StatePublished - 2005


  • Cluster analysis
  • Forecasting water demand
  • Principal component analysis


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