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Open AccessFeature PaperArticle

Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand

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Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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Environmental Research and Studies Centre, University of Babylon, Babylon 51001, Iraq
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BEST Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK
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Department of Environment Engineering, University of Babylon, Babylon 51001, Iraq
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Department of Mechanical Engineering, Wasit University, Wasit 52001, Iraq
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Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
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Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College Middle Technical University, Al Doura, Baghdad 10022, Iraq
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Faculty of Science for Women, Babylon University, Babylon 51001, Iraq
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Author to whom correspondence should be addressed.
Water 2020, 12(10), 2692; https://doi.org/10.3390/w12102692
Received: 26 July 2020 / Revised: 20 September 2020 / Accepted: 24 September 2020 / Published: 26 September 2020
(This article belongs to the Special Issue Advanced Applications of Electrocoagulation in Water and Wastewater)
Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that 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 a 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. View Full-Text
Keywords: artificial neural network; backtracking search algorithm; empirical mode decomposition; multi-verse optimiser; slime mould algorithm; water demand model artificial neural network; backtracking search algorithm; empirical mode decomposition; multi-verse optimiser; slime mould algorithm; water demand model
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Zubaidi, S.L.; Abdulkareem, I.H.; Hashim, K.S.; Al-Bugharbee, H.; Ridha, H.M.; Gharghan, S.K.; Al-Qaim, F.F.; Muradov, M.; Kot, P.; Al-Khaddar, R. Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand. Water 2020, 12, 2692.

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