Forecasting urban water demand using different hybrid-based metaheuristic algorithms' inspire for extracting artificial neural network hyperparameters.

Salah L Zubaidi, Hussein Al-Bugharbee, Ali W Alattabi, Hussein Mohammed Ridha, Khalid Hashim, Nadhir Al-Ansari, Zaher Mundher Yaseen

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Salah L ZubaidiDepartment of Civil Engineering, Wasit University, Wasit, 52001, Iraq. salahlafta@uowasit.edu.iq.

Hussein Al-BugharbeeDepartment of Mechanical Engineering, Wasit University, Wasit, 52001, Iraq.

Ali W AlattabiDepartment of Civil Engineering, Wasit University, Wasit, 52001, Iraq.

Hussein Mohammed RidhaAdvanced Lightning, Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia.

Khalid HashimDepartment of Environmental Engineering, University of Babylon, Al‑Hillah, 51001, Iraq.

Nadhir Al-AnsariDepartment of Civil Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87, Lulea, Sweden. nadhir.alansari@ltu.se.

Zaher Mundher YaseenCivil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ANNMetaheuristic algorithmsPSOGAUrban water managementWater demand prediction

Identifiers

PMID39402113

DOI10.1038/s41598-024-73002-w

PMCPMC11473751

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