Weekly urban water demand forecasting using a hybrid wavelet–bootstrap–artificial neural network approach

Abstract Weekly urban water demand forecasting using a hybrid wavelet-bootstrap-artificial neural network approach. This study developed a hybrid wavelet-bootstrap-artificial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis significantly improved the model’s performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands; this can be helpful in operational water demand forecasting.