Municipal Water Demand Forecasting: Tools for Intervention Time Series

Abstract

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.

Keywords:

• ARIMA models
• Hybrid models
• Intervention analysis
• Neural networks
• Water demand

Mathematics Subject Classification:

• 62P30
• 37M10
• 62M45

Acknowledgments

This work has been supported by project IDAWAS, DPI2009-11591, of the Dirección General de Investigación of the Ministerio de Ciencia e Innovación of Spain, and ACOMP/2010/146 of the Consellería de Educación of the Generalitat Valenciana. As well, the authors are grateful to “Aguas de Murcia” for the collaboration in this work and for the availability of the data.