Utilizing Clonal Selection Theory Inspired Algorithms and K-Means Clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption
Ayodele Lasisi, Rozaida Ghazali, Haruna Chiroma · Advances in intelligent systems and computing · 2016
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.