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Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

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Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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Department of Applied Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
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Department of Mechanical Engineering, Wasit University, Wasit 52001, Iraq
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Built Environment and Sustainable Technologies (BEST) Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK
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Department of Environment Engineering, Babylon University, Babylon 51001, Iraq
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Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq
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Author to whom correspondence should be addressed.
Water 2020, 12(7), 1885; https://doi.org/10.3390/w12071885
Received: 24 May 2020 / Revised: 25 June 2020 / Accepted: 26 June 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Advanced Applications of Electrocoagulation in Water and Wastewater)
The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth. View Full-Text
Keywords: artificial neural network; backtracking search algorithm; municipal water demand; climate change; population growth artificial neural network; backtracking search algorithm; municipal water demand; climate change; population growth
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Zubaidi, S.L.; Ortega-Martorell, S.; Al-Bugharbee, H.; Olier, I.; Hashim, K.S.; Gharghan, S.K.; Kot, P.; Al-Khaddar, R. Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study. Water 2020, 12, 1885.

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