Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.