Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 2
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.
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Acknowledgments
This study formed part of a thesis submitted for the degree of Master of Philosophy in Engineering for Sustainable Development at the Cambridge-MIT Institute at the University of Cambridge. Funding for part of these studies was provided by the Cambridge Commonwealth Trust of the University of Cambridge and this is gratefully acknowledged. The writers also wish to thank Dr. Richard Fenner of the Center for Sustainable Development at the University of Cambridge for his enthusiastic support and valuable advice throughout the course of this research. Dr. Fenner was also responsible for the idea of testing the usefulness of a weighted system of antecedent days of rainfall. Data were provided by Mr. John Bougadis of Delcan Corporation in Ottawa, Canada.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 2 • March 2008
Pages: 119 - 128
Copyright
© 2008 ASCE.
History
Received: Jan 25, 2006
Accepted: May 21, 2007
Published online: Mar 1, 2008
Published in print: Mar 2008
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