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• W2001168879 abstract "Water demand forecasts are needed for the design, operation and management of urban water supply systems. In this study, the relative performance of regression, time series analysis and artificial neural network (ANN) models are investigated for short-term peak water demand forecasting. The significance of climatic variables (rainfall and maximum air temperature, in addition to past water demand) on water demand management is also investigated. Numerical analysis was performed on data from the city of Ottawa, Ontario, Canada. The existing water supply infrastructure will not be able to meet the demand for projected population growth; thus, a study is needed to determine the effect of peak water demand management on the sizing and staging of facilities for developing an expansion strategy. Three different ANNs and regression models and seven time-series models have been developed and compared. The ANN models consistently outperformed the regression and time-series models developed in this study. It has been found that water demand on a weekly basis is more significantly correlated with the rainfall amount than the occurrence of rainfall. Copyright © 2005 John Wiley & Sons, Ltd." @default.
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• W2001168879 date "2005-01-01" @default.
• W2001168879 modified "2023-10-17" @default.
• W2001168879 title "Short-term municipal water demand forecasting" @default.
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• W2001168879 doi "https://doi.org/10.1002/hyp.5763" @default.
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