Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm.

Autor: Chiroma H; Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur, Malaysia; School of Science, Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria., Abdul-kareem S; Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur, Malaysia., Khan A; Software and multimedia center faculty of science and computer technology, University Tun Hussein Onn, Johor Bahru, Malaysia., Nawi NM; Software and multimedia center faculty of science and computer technology, University Tun Hussein Onn, Johor Bahru, Malaysia., Gital AY; Faculty of Computing, University Technology Malaysia, Johor Bahru, Malaysia., Shuib L; Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur, Malaysia., Abubakar AI; Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia., Rahman MZ; Software and multimedia center faculty of science and computer technology, University Tun Hussein Onn, Johor Bahru, Malaysia., Herawan T; Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur, Malaysia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2015 Aug 25; Vol. 10 (8), pp. e0136140. Date of Electronic Publication: 2015 Aug 25 (Print Publication: 2015).
DOI: 10.1371/journal.pone.0136140
Abstrakt: Background: Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.
Methods/findings: The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.
Conclusion: An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks--hence, reducing global warming. The policy implications are discussed in the paper.
Databáze: MEDLINE
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