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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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A Novel Approach for Predicting Monthly Water Demand by Combining Singular Spectrum Analysis with Neural Networks.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
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 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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