A Novel Approach for Predicting Monthly Water Demand by Combining Singular Spectrum Analysis with Neural Networks

Zubaidi, S, Dooley, J, Alkhaddar, R, Abdellatif, M, Al-Bugharbee, H and Ortega-Martorell, S (2018) A Novel Approach for Predicting Monthly Water Demand by Combining Singular Spectrum Analysis with Neural Networks. Journal of Hydrology, 561. pp. 136-145. ISSN 0022-1694

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Abstract

Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables.

Item Type:	Article
Uncontrolled Keywords:	MD Multidisciplinary
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TD Environmental technology. Sanitary engineering
Divisions:	Applied Mathematics (merged with Comp Sci 10 Aug 20) Civil Engineering (merged with Built Env 10 Aug 20)
Publisher:	Elsevier
Date of acceptance:	17 March 2018
Date of first compliant Open Access:	31 July 2019
Date Deposited:	16 Apr 2018 09:07
Last Modified:	04 Sep 2021 10:33
DOI or ID number:	10.1016/j.jhydrol.2018.03.047
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8485

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