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

Amos Anele,Ezio Todini,Adnan Abu-Mahfouz,Yskandar Hamam

doi:10.3390/w9110887

Abstract

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.

Highlights

The most crucial factor in the planning, operation and management of water distribution systems (WDS) is the satisfaction of consumer demand
As regards the selection of accurate and appropriate forecasting models for short-term water demand (STWD) prediction, this section of the paper presents recommendations and future work based on the forecasts generated by AR, moving average (MA), autoregressive-moving average (ARMA), ARMA with exogenous variable (ARMAX), feed-forward back-propagation neural network (FFBP-NN), and hybrid models
This study shows that univariate time series (UTS) models (i.e., ARMA), time series regression (TSR) models (i.e., ARMAX), and hybrid model may be considered as the accurate and appropriate models for STWD prediction

Summary

Introduction

The most crucial factor in the planning, operation and management of water distribution systems (WDS) is the satisfaction of consumer demand. Water utilities need accurate and appropriate short-term water demand (STWD) forecasts in order to continually satisfy consumers with quality water in adequate volumes, and at reasonable pressures [5,6,7]. Water 2017, 9, 887; doi:10.3390/w9110887 www.mdpi.com/journal/water helping utilities plan and manage water demands for near-term events optimizing daily operations of the infrastructure (e.g., pump scheduling, control of reservoirs volume, pressure management, and water conservation program). As regards the selection of an accurate and appropriate model, the third objective of the paper is to present recommendations and future work for the forecasts generated by the forecasting models considered

Objectives

Methods

Discussion

Conclusion