World Greenhouse Gas Emission Classification Using Support Vector Machine (Svm) Method

Abstract

The phenomenon of Heatwaves has struck several countries across the globe due to climate change. This climate change has led to an increase in greenhouse gas emissions surpassing the limits set by the IPCC Fourth Assessment Report GWPs. This study utilizes the Support Vector Machine (SVM) classification method to identify and categorize greenhouse gas emission data from 1990 to 2020 using four kernels function such as linear, polynomial, radial basis function (RBF), and sigmoid. The SVM method demonstrates excellent performance in constructing classification models with a polynomial kernel function. This is evidenced by high values of training accuracy, testing accuracy, and F1-score, accompanied by short training and testing analysis times. Successively, these values are 97.39%, 97.69%, 96.82%, 0.59 seconds, and 0.22 seconds.