Prediction of OPEC Carbon Dioxide Emissions Using K-Means Clustering and Ensemble Algorithm

The rapid increase in the concentration of carbon dioxide ( \(\text {CO}_2\) ) polluting the atmosphere induces global warming and climate change. This is detrimental to human health and their natural habitat. Thus, it is imperative to proffer measures in analyzing and predicting the emissions of \(\text {CO}_2\) . This research suggests using an ensemble approach with fuzzy nearest neighbor, sequential minimal optimization, and logistic regression to predict global \(\text {CO}_2\) emissions. The K-means algorithm divides data into groups of similar and relevant patterns. Simulation findings show that the proposed model outperforms techniques such as multi-layer perceptron, fuzzy ownership nearest neighbor, and random forest. It also improves \(\text {CO}_2\) forecast accuracy.