Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand

Zubaidi, Salah L, Al-Bdairi, Nabeel Saleem Saad, Ortega-Martorell, Sandra ORCID: 0000-0001-9927-3209, Ridha, Hussein Mohammed, Al-Ansari, Nadhir, Al-Bugharbee, Hussein, Hashim, Khalid and Gharghan, Sadik Kamel (2023) Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 149 (1). 04022075-.

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Official URL: http://dx.doi.org/10.1061/(asce)wr.1943-5452.00016...

Abstract

Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors).

Item Type:	Article
Uncontrolled Keywords:	Baghdad City, Climatic factors, Machine learning, Metaheuristic algorithm, Water demand model
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	18 May 2023 09:25
Last Modified:	15 Mar 2024 16:31
DOI:	10.1061/(ASCE)WR.1943-5452.0001602
Open Access URL:	https://doi.org/10.1061/(ASCE)WR.1943-5452.0001602
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170472