Open AccessForecasting of monthly stochastic signal of urban water demand: Baghdad as a case study
Author(s) -
Salah L. Zubaidi,
Hussein Al-Bugharbee,
Yousif Raad Muhsin,
Khalid Hashim,
Rafid Alkhaddar
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/888/1/012018
Subject(s) - autoregressive model , signal (programming language) , water consumption , consumption (sociology) , water scarcity , water resources , water extraction , process (computing) , computer science , econometrics , environmental science , water resource management , extraction (chemistry) , economics , programming language , ecology , social science , chemistry , chromatography , sociology , biology , operating system
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.