A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Salah L. Zubaidi (), Sandra Ortega-Martorell, Patryk Kot, Rafid M. Alkhaddar, Mawada Abdellatif, Sadik K. Gharghan, Maytham S. Ahmed and Khalid Hashim
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Salah L. Zubaidi: University of Wasit
Sandra Ortega-Martorell: Liverpool John Moores University
Patryk Kot: Liverpool John Moores University
Rafid M. Alkhaddar: Liverpool John Moores University
Mawada Abdellatif: Liverpool John Moores University
Sadik K. Gharghan: Electrical Engineering Technical College Middle Technical University (MTU)
Maytham S. Ahmed: General Directorate of Electrical Energy Production-Basrah, Ministry of Electricity
Khalid Hashim: Liverpool John Moores University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 3, No 20, 1265-1279

Abstract: Abstract The accurate forecast of water demand is challenging for water utilities, specifically when considering the implications of climate change. As such, this is the first study that focuses on finding associations between monthly climate factors and municipal water consumption, using baseline data collected between 1980 and 2010. The aim of the study was to investigate the reliability and capability of a combination of techniques, including Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANNs), to accurately predict long-term, monthly water demands. The principal findings of this research are as follows: a) SSA is a powerful method when applied to remove the impact of socio-economic variables and noise, and to determine a stochastic signal for long-term water consumption time series; b) ANN performed better when optimised using the Lightning Search Algorithm (LSA-ANN) compared with other approaches used in previous studies, i.e. hybrid Particle Swarm Optimisation (PSO-ANN) and Gravitational Search Algorithm (GSA-ANN); c) the proposed LSA-ANN methodology was able to produce a highly accurate and robust model of water demand, achieving a correlation coefficient of 0.96 between observed and predicted water demand when using a validation dataset, and a very small root mean square error of 0.025.

Keywords: Artificial neural network; Climate change; Lightning search algorithm; Singular Spectrum analysis and water prediction (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-020-02500-z

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