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

Abstract

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.

Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

“…As the most recent MEA participant, SMA operates an easy and easyto-use mechanism. The algorithm [26] has been commonly used since it was proposed [77], [81], [113], such as feature selection [78], energy management [114]. In addition, several improved versions of SMA were introduced, for example, hybrid SMA-based whale optimization algorithm [81], adaptive guided differential evolution algorithm with SMA [77], chaos-opposition-enhanced SMA [115], multiobjective SMA Based on elitist non-dominated sorting [116], and improved SMA with Levy flight [117].…”

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

“…It is noted that the process of fine-tuning a machine learning model can be formulated as a global optimization problem. In recent years, various metaheuristic approaches have been successfully employed, including monarch butterfly optimization [62,63], slime mould algorithm [64], moth search algorithm [65,66], Harris hawks optimization [67][68][69][70], differential flower pollination [71], symbiotic organisms search [72], Henry gas solubility optimization [73], and satin bowerbird optimizer [74]. As can be seen from the literature, there is an increasing trend of hybridizing metaheuristics and machine learning to tackle complex problems in the field of engineering.…”

Sustainability assessment of wastewater treatment techniques in urban areas of Iraq using multi-criteria decision analysis (MCDA)

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