Zubaidi, SL, Al-Bugharbee, H, Alattabi, AW, Ridha, HM, Hashim, K, Al-Ansari, N and Yaseen, ZM (2024) Forecasting urban water demand using different hybrid-based metaheuristic algorithms’ inspire for extracting artificial neural network hyperparameters. Scientific Reports, 14 (1).

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Abstract

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms’ behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing. Each model performed adequately in simulating the fundamental dynamics of monthly urban water demand as it relates to meteorological variables, proving that they were all successful. Statistical fitness measures showed that PSOGA-ANN outperformed competing algorithms.

Item Type:	Article
Uncontrolled Keywords:	ANN; Metaheuristic algorithms; PSOGA; Urban water management; Water demand prediction
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Civil Engineering and Built Environment
Publisher:	Nature Research
SWORD Depositor:	A Symplectic
Date Deposited:	30 Oct 2024 12:08
Last Modified:	30 Oct 2024 12:15
DOI or ID number:	10.1038/s41598-024-73002-w
URI:	https://researchonline.ljmu.ac.uk/id/eprint/24621

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