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Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy

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  • Salvatore Campisi-Pinto
  • Jan Adamowski
  • Gideon Oron

Abstract

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, Haar and Daubechies of type db2, db3, db4, and db5) on a single neural network. Empirical results show that neural networks coupled with Haar and Daubechies’ filter-banks of type db2 and db3 outperformed all of the following: non-coupled ANN, Multiple Linear Regression and ANN models coupled with Daubechies filters of type db4 and db5. The results of this study suggest that reduced variance in the training-set (by means of denoising) may improve forecasting accuracy; on the other hand, an oversimplification of the input-matrix may deteriorate forecasting accuracy and induce network instability. Copyright Springer Science+Business Media B.V. 2012

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  • Salvatore Campisi-Pinto & Jan Adamowski & Gideon Oron, 2012. "Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3539-3558, September.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:12:p:3539-3558
    DOI: 10.1007/s11269-012-0089-y
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    References listed on IDEAS

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    6. Chien-ming Chou, 2011. "A Threshold Based Wavelet Denoising Method for Hydrological Data Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(7), pages 1809-1830, May.
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    3. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.
    4. Yanhu He & Jie Yang & Xiaohong Chen & Kairong Lin & Yanhui Zheng & Zhaoli Wang, 2018. "A Two-stage Approach to Basin-scale Water Demand Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 401-416, January.
    5. Qinghua Zhang & Yanfang Diao & Jie Dong, 2013. "Regional Water Demand Prediction and Analysis Based on Cobb-Douglas Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3103-3113, June.
    6. Iman Fatehi & Bahman Amiri & Afshin Alizadeh & Jan Adamowski, 2015. "Modeling the Relationship between Catchment Attributes and In-stream Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5055-5072, November.
    7. Izabela Rojek, 2014. "Models for Better Environmental Intelligent Management within Water Supply Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3875-3890, September.
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    10. Bahaa Khalil & Taha Ouarda & André St-Hilaire, 2012. "Comparison of Record-Extension Techniques for Water Quality Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4259-4280, November.
    11. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.

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