Prediction of air pollution index (API) using support vector machine (SVM)

The existing methods of calculating air pollution index are complex and time consuming. Therefore new accurate and efficient modeling techniques need to be proposed. Thus, a support vector machine is proposed in this study to model the air pollution index. There are three main parameters affecting the performance of the support vector machine model: penalty factor (C), regularization parameter (epsilon) and the type of kernel function used. However, in this study, only kernel functions model parameters are investigated. The results of the model are analyzed by using sum of squares error (SSE), mean of sum of squares error (MSSE) and coefficient of determination (R-2). It is found that the proposed model using radial basis function (RBF) kernel function effectively and accurately able to solve the problem of complex air pollution index modeling with sum square error (SSE), mean sum square error (MSSE) and coefficient of determination (R-2) of 2008, 3.1.4440 and 0.9843 respectively.