The Prediction of Municipal Water Demand in Iraq: A Case Study of Baghdad Governorate

Abstract

Accurate prediction of short-term water demand plays an important role for water suppliers as well as for government's water plan. This paper aims to predict a municipal water demand for an upcoming year based on previous water consumption in Baghdad city. We have investigated various signal processing approaches to address the noisy time series data of water consumption, while a new methodology for short-term prediction of municipal water consumption has been proposed. This would enable us to forecast the short-term municipal water demand using different windows and multi-stages of hybrid univariate singular spectrum analysis and autoregressive model (SSA-AR model). First, different windows and multi-stages of SSA are utilised to analyse and clean the original water time series from noise. Then, the autoregressive (AR) model is employed to predict water demand based on the treated water consumption time series. In this study, monthly water consumption data from (2006-2015) for Al-Wehda treatment plant in Baghdad city, Iraq is selected to assess the model. The findings show that (SSA-AR model) can predict water demand with high accuracy from high noisy raw data.