Utilizing Clonal Selection Theory Inspired Algorithms and K-Means Clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption
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.
KeywordsClonal selection algorithm Artificial immune recognition system Clustering algorithm Carbon dioxide emissions
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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