Open Access

ARTICLE

Modeling CO₂ Emission of Middle Eastern Countries Using Intelligent Methods

Mamdouh El Haj Assad¹, Ibrahim Mahariq^2,*, Zaher Al Barakeh², Mahmoud Khasawneh², Mohammad Ali Amooie³

1 Sustainable and Renewable Energy Engineering Department, University of Sharjah, PO Box 27272, Sharjah, UAE
2 College of Engineering and Technology, American University of the Middle East, Kuwait
3 Department of Mechanical and Industrial Engineering, Louisiana State University, USA

* Corresponding Author: Ibrahim Mahariq. Email:

(This article belongs to the Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)

Computers, Materials & Continua 2021, 69(3), 3767-3781. https://doi.org/10.32604/cmc.2021.018872

Received 23 March 2021; Accepted 24 April 2021; Issue published 24 August 2021

Abstract

CO₂ emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities. Consequently, these factors must be considered for CO₂ emission prediction for seven middle eastern countries including Iran, Kuwait, United Arab Emirates, Turkey, Saudi Arabia, Iraq and Qatar. In order to propose a predictive model, a Multilayer Perceptron Artificial Neural Network (MLP ANN) is applied. Three transfer functions including logsig, tansig and radial basis functions are utilized in the hidden layer of the network. Moreover, various numbers of neurons are applied in the structure of the models. It is revealed that using MLP ANN makes it possible to accurately predict CO₂ emission of these countries. In addition, it is concluded that using logsig transfer function leads to the highest accuracy with minimum value of mean squared error (MSE) which is followed by the networks with radial basis and tansig transfer functions. The R-squared of the networks with logsig, radial basis and tansig transfer functions are 0.9998, 0.9997 and 0.9996, respectively. Finally, comparison of the proposed model with a similar study, considered five countries in the same region, reveals higher accuracy in term of MSE.

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Citations