Forecasting of monthly stochastic signal of urban water demand: Baghdad as a case study

Abstract

Abstract Forecasting of municipal water demand is essential for the decision-making process in the water industry in particular for countries that suffered from water scarcity. An accurate prediction of water demand improves the water distribution systems’ performance. This study analyses the water consumption data of Baghdad city using a signal pre-treatment processing approach aiming at a stochastic signal extraction of such data. An autoregressive (AR) model is then applied to predict monthly water consumption. Our prediction model has been trained and tested using a water consumption data captured from Al-Wehda treatment plant between 2006 and 2015. The results reveal that applying signal pre-treatment method was an effective approach for detecting stochastics of our water consumption data, and the hybrid model was reliable for the prediction of water demand.