Zubaidi, S, Ortega Martorell, S, Kot, P, Al Khaddar, RM, Abdellatif, M, Gharghan, S, Ahmed, M and Hashim, KS (2020) A Method for Predicting Long-term Municipal Water Demands Under Climate Change. Water Resources Management, 34. pp. 1265-1279. ISSN 0920-4741

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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.

Item Type:	Article
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in Water Resources Management. The final authenticated version is available online at: https://doi.org/10.1007/s11269-020-02500-z
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > T Technology (General)
Divisions:	Applied Mathematics (merged with Comp Sci 10 Aug 20) Civil Engineering (merged with Built Env 10 Aug 20)
Publisher:	Springer Verlag
Date Deposited:	21 Jan 2020 11:47
Last Modified:	04 Sep 2021 08:06
URI:	https://researchonline.ljmu.ac.uk/id/eprint/12067

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