文章基本信息

标题：Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural networ
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作者：Ali Komeili Birjandi ; Morteza Fahim Alavi ; Mohamed Salem 等
期刊名称：Intl Jnl of Low-Carbon Technologies
印刷版ISSN：1748-1317
电子版ISSN：1748-1325
出版年度：2022
卷号：17
页码：321-326
DOI：10.1093/ijlct/ctac002/6540278
语种：English
出版社：Oxford University Press
摘要：Energy and economy play a substantial role in environmental issues such as the emission of greenhouse gases. CO2 is one of the greenhouses that is hugely produced in industrial processes and other human being activities. The major share of CO2 emission is related to the energy-related activities. As a result, modeling the amount of produced CO2 by utilization of different energy sources must be considered. Moreover, by considering economic indicators such as gross domestic product, the accuracy of the model could be improved. In the present work, artificial neural network (ANN) with two transfer functions including normalized radial basis and tansig is used to model CO2 production of different countries in Southeast Asia including Malaysia, Indonesia, Singapore and Vietnam. It is observed that using the network with normalized radial basis and 11 neurons in the hidden layer provides the model with the highest precision with an R2 of 0.9997 while the optimal architecture of the network using tansig function provides a model with R2 of 0.9996.