An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada. Issue 2 (7th February 2017)

• Record Type: Journal Article
• Authors: Tiwari, Mukesh K.; Adamowski, Jan F.
• Is Part Of: Urban water journal. Volume 14:Issue 2(2017)
• Page Start: 185
• Page End: 201
• Publication Date: 2017-02-07
• Subjects: Water demand forecasting -- artificial neural networks -- uncertainty -- bootstrap -- wavelets -- Canada; Municipal water supply -- Management -- Periodicals; Water-supply -- Planning -- Periodicals; 628.1
• DOI: 10.1080/1573062X.2015.1084011
• Languages: English
• ISSNs: 1573-062X

Abstract

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.