Preprint Article Version 1 This version is not peer-reviewed

Carbon Dioxide Emission Prediction of Four CIS Countries by Applying a Correlation and GMDH Artificial Neural Network

Version 1 : Received: 21 June 2019 / Approved: 22 June 2019 / Online: 22 June 2019 (16:17:02 CEST)

How to cite: Ahmadi, M.H.; Ramezanizadeh, M.; Alhuyi Nazari, M.; Kheradmand, S.; Shamshirband, S. Carbon Dioxide Emission Prediction of Four CIS Countries by Applying a Correlation and GMDH Artificial Neural Network. Preprints 2019, 2019060227 (doi: 10.20944/preprints201906.0227.v1). Ahmadi, M.H.; Ramezanizadeh, M.; Alhuyi Nazari, M.; Kheradmand, S.; Shamshirband, S. Carbon Dioxide Emission Prediction of Four CIS Countries by Applying a Correlation and GMDH Artificial Neural Network. Preprints 2019, 2019060227 (doi: 10.20944/preprints201906.0227.v1).

Abstract

Increase in the emission of Greenhouse Gases (GHS) is among the significant concerns of government, societies, and policymakers. Due to the highest share of carbon dioxide in the produced GHGs, it is necessary to assess the factors that influence its emission. Energy systems and economic activities noticeably influence the amount of carbon dioxide production of countries. In this article, Artificial Neural Network (ANN) in addition to a linear correlation used to predict carbon dioxide emission of four CIS countries, including Turkmenistan, Uzbekistan, Kazakhstan, and Azerbaijan based the consumption of various energy sources and GDP, as the economic indicator. According to the obtained data by the proposed models, carbon dioxide emission can be accurately estimated by utilizing the mentioned input data. Models’ R-squared value are 0.9997 and 0.9999 in the cases of applying the correlation and ANN-based model. Moreover, the average absolute relative deviations by utilizing the correlation and GMDH ANN are approximately 1.05% and 0.61%, respectively. These statistical values demonstrate more proper performance of the ANN-based model compared with the applied linear correlation.

Subject Areas

artificial neural network; carbon dioxide; greenhouse gases; GMDH; GDP

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