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Utilizing Clonal Selection Theory Inspired Algorithms and K-Means Clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 549))

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Abstract

The prediction of carbon dioxide (CO2) emissions from petroleum consumption inspired and motivated this research. Over the years, the rate of emissions of CO2 continues to multiply, resulting in global warming. This paper thus proposes the use of clonal selection theory inspired algorithms; CLONALG and AIRS to forecast global CO2 emissions. The K-means algorithm divides the data into groups of similar and meaningful patterns. Comparative simulations with multi-layer Perceptron, IBk, fuzzy-rough nearest neighbor, and vaguely quantified nearest neighbor reveal that the CLONALG and AIRS produced outstanding results, and are able to generate highest detection rates and lowest false alarm rates. As such, gathering useful information with the accurate prediction of CO2 emissions can help to reduce the emission of CO2 contributions to global warming which assist in policies on climate change.

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Acknowledgements

This work is supported by the Office for Research, Innovation, Commercialization, and Consultancy Management (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM), and Ministry of Higher Education (MOHE) Malaysia under the Fundamental Research Grant Scheme (FRGS) Vote No. 1235.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Ayodele Lasisi & Rozaida Ghazali

  2. Department of Computer Science, School of Science, Federal College of Education (Technical), Gombe, Gombe State, Nigeria

    Haruna Chiroma

Authors
  1. Ayodele Lasisi
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  2. Rozaida Ghazali
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  3. Haruna Chiroma
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Corresponding author

Correspondence to Ayodele Lasisi .

Editor information

Editors and Affiliations

  1. Department of Information System, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  3. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  4. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

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Lasisi, A., Ghazali, R., Chiroma, H. (2017). Utilizing Clonal Selection Theory Inspired Algorithms and K-Means Clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-51281-5_11

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