American Society of Civil Engineers

Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks

by Jan Franklin Adamowski, (Postdoctoral Researcher, Cyprus Inst. Program for Energy, Envir., and Water Resour., Massachusetts Inst. of Technol., 77 Massachusetts Avenue, E40-469, Cambridge, MA 02139; formerly, Inst. of Envir. Engrg. Systems, Warsaw Univ. of Technol., ul. Nowowiejska 20, Warsaw, Poland 00-653. E-mail:

Journal of Water Resources Planning and Management, Vol. 134, No. 2, March/April 2008, pp. 119-128, (doi:

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Document type: Journal Paper
Abstract: Peak daily water demand forecasts are required for the cost-effective and sustainable management and expansion of urban water supply infrastructure. This paper compares multiple linear regression, time series analysis, and artificial neural networks (ANNs) as techniques for peak daily summer water demand forecast modeling. Analysis was performed on 10 years of peak daily water demand data and meteorological variables (maximum daily temperature and daily rainfall) for the summer months of May to August of each year for an area of high outdoor water usage in the city of Ottawa, Canada. Thirty-nine multiple linear regression models, nine time series models, and 39 ANN models were developed and their relative performance was compared. The artificial neural network approach is shown to provide a better prediction of peak daily summer water demand than multiple linear regression and time series analysis. The best results were obtained when peak water demand from the previous day, maximum temperature from the current and previous day, and the occurrence/nonoccurrence of rainfall from five days before, were used as input data. It was also found that the peak daily summer water demand is better correlated with the rainfall occurrence rather than the amount of rainfall itself, and that assigning a weighting system to the antecedent days of no rainfall does not result in more accurate models.

ASCE Subject Headings:
Artificial intelligence
Municipal water
Regression analysis
Time series analysis
Water demand
Water resources