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AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS

LAZIM ABDULLAH

School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Malaysia

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and

HERRINI MOHD PAUZI

School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Malaysia

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https://doi.org/10.1142/S146902681450014XCited by:4 (Source: Crossref)

Abstract

This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide (CO₂) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO₂ intensity were employed in developing all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the LM algorithm outperformed both BFGS and GD algorithms.

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