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Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

1
Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa
2
Council for Scientific and Industrial Research, Pretoria 0081, South Africa
3
Department of Biological, Geological and Environmental Sciences, University of Bologna, Via Zamboni, 33-40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
Water 2017, 9(11), 887; https://doi.org/10.3390/w9110887
Received: 8 September 2017 / Revised: 6 November 2017 / Accepted: 6 November 2017 / Published: 13 November 2017
The stochastic nature of water consumption patterns during the day and week varies. Therefore, to continually provide water to consumers with appropriate quality, quantity and pressure, water utilities require accurate and appropriate short-term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times series models (i.e., autoregressive (AR), moving average (MA), autoregressive-moving average (ARMA), and ARMA with exogenous variable (ARMAX)) introduced by Box and Jenkins (1970), feed-forward back-propagation neural network (FFBP-NN), and hybrid model (i.e., combined forecasts from ARMA and FFBP-NN) are compared with each other for a common set of data. Akaike information criterion (AIC), originally proposed by Akaike (1974) is used to estimate the quality of each short-term forecasting model. Furthermore, Nash–Sutcliffe (NS) model efficiency coefficient proposed by Nash–Sutcliffe (1970), root mean square error (RMSE) and mean absolute percentage error (MAPE) are the forecasting statistical terms used to assess the predictive performance of the models. Lastly, as regards the selection of an accurate and appropriate STWD forecasting model, this paper provides recommendations and future work based on the forecasts generated by each of the predictive models considered. View Full-Text
Keywords: forecasting models; short-term; water demand simulation forecasting models; short-term; water demand simulation
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MDPI and ACS Style

Anele, A.O.; Hamam, Y.; Abu-Mahfouz, A.M.; Todini, E. Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction. Water 2017, 9, 887.

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