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Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries.

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Title: Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries.
Source: International Journal of Low Carbon Technologies. Sep2018, Vol. 13 Issue 3, p266-271. 6p. 8 Graphs.
Subjects: Artificial neural networks, GMDH algorithms, Emissions (Air pollution), Carbon dioxide & the environment, Gross domestic product, Renewable energy sources
Abstract: CO2 emission depends on several parameters. Due to environmental issues, it is necessary to find influential factors on CO2 emission as one of the most critical greenhouse gases. Type of utilized fuels and their share in total primary energy consumption, Gross Domestic Product (GDP) as an indicator for economic activities and the share of renewable energies play key role in the amount of CO2 emission. In the present study, Group method of data handling (GMDH) is applied in order to model CO2 emission as a function of consumption of various fuels, renewable energies and GDP. Obtained data showed that GMDH is an appropriate approach to predict CO2 emission. Comparing between actual data and GMDH output indicates that the R -squared value for the proposed model is equal to 0.998 which shows its high accuracy. In addition, it is observed that the highest absolute error by using GMDH artificial neural network is lower than 4%. The absolute relative error for more than 66% of data is lower than 1% which is another criterion demonstrating acceptable accuracy of the proposed model. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Low Carbon Technologies is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Low+Carbon+Technologies%22">International Journal of Low Carbon Technologies</searchLink>. Sep2018, Vol. 13 Issue 3, p266-271. 6p. 8 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22GMDH+algorithms%22">GMDH algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Emissions+%28Air+pollution%29%22">Emissions (Air pollution)</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+dioxide+%26+the+environment%22">Carbon dioxide & the environment</searchLink><br /><searchLink fieldCode="DE" term="%22Gross+domestic+product%22">Gross domestic product</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: CO2 emission depends on several parameters. Due to environmental issues, it is necessary to find influential factors on CO2 emission as one of the most critical greenhouse gases. Type of utilized fuels and their share in total primary energy consumption, Gross Domestic Product (GDP) as an indicator for economic activities and the share of renewable energies play key role in the amount of CO2 emission. In the present study, Group method of data handling (GMDH) is applied in order to model CO2 emission as a function of consumption of various fuels, renewable energies and GDP. Obtained data showed that GMDH is an appropriate approach to predict CO2 emission. Comparing between actual data and GMDH output indicates that the R -squared value for the proposed model is equal to 0.998 which shows its high accuracy. In addition, it is observed that the highest absolute error by using GMDH artificial neural network is lower than 4%. The absolute relative error for more than 66% of data is lower than 1% which is another criterion demonstrating acceptable accuracy of the proposed model. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Low Carbon Technologies is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1093/ijlct/cty026
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 6
        StartPage: 266
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: GMDH algorithms
        Type: general
      – SubjectFull: Emissions (Air pollution)
        Type: general
      – SubjectFull: Carbon dioxide & the environment
        Type: general
      – SubjectFull: Gross domestic product
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
    Titles:
      – TitleFull: Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries.
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            – D: 01
              M: 09
              Text: Sep2018
              Type: published
              Y: 2018
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              Value: 13
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            – TitleFull: International Journal of Low Carbon Technologies
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