Application of short-term water demand prediction model to Seoul

C. N. Joo, J. Y. Koo, M. J. Yu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

Original languageEnglish
Pages (from-to)255-261
Number of pages7
JournalWater Science and Technology
Issue number6-7
StatePublished - 2002


  • Artificial neural network
  • Cross correlation
  • Multiple regression
  • Short-term water demand


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