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Hybridised Artificial Neural Network model with Slime Mould Algorithm: A novel methodology for prediction urban stochastic water demand

Zubaidi, SL, Abdulkareem, IH, Hashim, KS, Al-Bugharbee, H, Ridha, HM, Gharghan, SK, Al-Qaim, FF, Muradov, M, Kot, P and Alkhaddar, R Hybridised Artificial Neural Network model with Slime Mould Algorithm: A novel methodology for prediction urban stochastic water demand. Water. ISSN 2073-4441 (Accepted)

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Abstract

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty which results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using Empirical Mode Decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the Artificial Neural Network (ANN) model was optimised by an up-to-date Slime Mould Algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms Multi-Verse Optimiser and Backtracking Search Algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Civil Engineering (merged with Built Env 10 Aug 20)
Publisher: MDPI AG
Date Deposited: 24 Sep 2020 11:19
Last Modified: 28 Sep 2020 08:00
URI: https://researchonline.ljmu.ac.uk/id/eprint/13719

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