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

摘要

The prediction of carbon dioxide (CO_2) emissions from petroleum consumption inspired and motivated this research. Over the years, the rate of emissions of CO_2 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 CO_2 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 CO_2 emissions can help to reduce the emission of CO_2 contributions to global warming which assist in policies on climate change.