An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary,Canada |
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Authors: | Mukesh K Tiwari Jan F Adamowski |
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Affiliation: | 1. Department of Soil and Water Engineering, College of Agricultural and Technology, Anand Agricultural University, Godhra, Gujarat, India;2. Department of Bioresource Engineering, McGill University, Quebec, Canada |
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Abstract: | This paper explores a hybrid wavelet, bootstrap and neural network (WBNN) modeling approach for daily (1, 3 and 5 day) urban water demand forecasting in situations with limited data availability. This method was tested using 3 years of daily water demand and meteorological data for the city of Calgary, Alberta, Canada. The performance of the WBNN method was compared to that of three other methods: traditional neural networks (NN), wavelet NNs (WNN), and bootstrap-based NN (BNN) models. While the hybrid WBNN and WNN models equally provided 1-day lead-time forecasts of greater accuracy than those obtained with other methods, for longer lead-time (3- or 5-day) forecasts the WBNN model alone outperformed the other models. The confidence bands generated using the WBNN model displayed the uncertainty associated with the forecasts. |
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Keywords: | Water demand forecasting artificial neural networks uncertainty bootstrap wavelets Canada |
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