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Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

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  • Muhammad Al-Zahrani
  • Amin Abo-Monasar

Abstract

Water demand prediction is essential in any short or long-term management plans. For short-term prediction of water demand, climatic factors play an important role since they have direct influence on water consumption. In this paper, prediction of future daily water demand for Al-Khobar city in the Kingdom of Saudi Arabia is investigated. For this purpose, the combined technique of Artificial Neural Networks (ANNs) and time series models was constructed based on the available daily water consumption and climatic data. The paper covers the following: forecast daily water demand for Al-Khobar city, compare the performance of the ANNs [General Regression Neural Network (GRNN) model] technique to time series models in predicting water consumption, and study the ability of the combined technique (GRNN and time series) to forecast water consumption compared to the time series technique alone. Results indicate that combining time series models with ANNs model will give better prediction compared to the use of ANNs or time series models alone. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Muhammad Al-Zahrani & Amin Abo-Monasar, 2015. "Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3651-3662, August.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:10:p:3651-3662
    DOI: 10.1007/s11269-015-1021-z
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