Article ID: | iaor20124839 |
Volume: | 26 |
Issue: | 12 |
Start Page Number: | 3539 |
End Page Number: | 3558 |
Publication Date: | Sep 2012 |
Journal: | Water Resources Management |
Authors: | Oron Gideon, Campisi-Pinto Salvatore, Adamowski Jan |
Keywords: | neural networks, simulation, forecasting: applications, management, statistics: regression |
Forecasting urban water demand can be of use in the management of water utilities. For example, activities such as water‐budgeting, operation and maintenance of pumps, wells, reservoirs, and mains require quantitative estimations of water resources at specified future dates. In this study, we tackle the problem of forecasting urban water demand by means of back‐propagation artificial neural networks (ANNs) coupled with wavelet‐denoising. In addition, non‐coupled ANN and Linear Multiple Regression were used as comparison models. We considered the case of the municipality of Syracuse, Italy; for this purpose, we used a 7 year‐long time series of water demand without additional predictors. Six forecasting horizons were considered, from 1 to 6 months ahead. The main objective was to implement a forecasting model that may be readily used for municipal water budgeting. An additional objective was to explore the impact of wavelet‐denoising on ANN generalization. For this purpose, we measured the impact of five different wavelet filter‐banks (namely,