Municipal Water Demand Forecasting: Tools for Intervention Time Series

M. Herrera, J. C. García-Díaz, J. Izquierdo, R. Pérez-García

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish
Pages (from-to)998-1007
Number of pages10
JournalStochastic Analysis and Applications
Volume29
Issue number6
DOIs
StatePublished - Nov 2011
Externally publishedYes

Keywords

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

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