Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Hadeel E. Khairan, Salah L. Zubaidi (), Mustafa Al-Mukhtar, Anmar Dulaimi, Hussein Al-Bugharbee, Furat A. Al-Faraj and Hussein Mohammed Ridha
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Hadeel E. Khairan: Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
Salah L. Zubaidi: Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
Mustafa Al-Mukhtar: Civil Engineering Department, University of Technology-Iraq, Baghdad 10066, Iraq
Anmar Dulaimi: College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
Hussein Al-Bugharbee: Department of Mechanical Engineering, Wasit University, Wasit 52001, Iraq
Furat A. Al-Faraj: School of Engineering, University of Bolton, Bolton BL3 5AB, UK
Hussein Mohammed Ridha: Department of Computer Engineering, University of Al-Mustansiriyah, Baghdad 10001, Iraq

Sustainability, 2023, vol. 15, issue 19, 1-19

Abstract: Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error ( RMSE ), Nash–Sutcliffe model efficiency ( NSE ), coefficient of determination ( R 2 ), maximum absolute error ( MAE ), and normalised mean standard error ( NMSE ). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R 2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique.

Keywords: reference evapotranspiration; artificial neural network; metaheuristic algorithm; particle swarm optimisation–grey wolf optimiser algorithm; Iraq (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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