Comparisons between Logistic Regression and Support Vector Machine for Air Pollution Datasets Forecasting

Particular matter (PM10) studying and forecasting is necessary to control and reduce the damage of environment and human health. There are many pollutants as sources of air pollution (Co, So2, O 3, Nox, No, Wind Speed, and Ambient Temperature) may effect on PM10 variable. PM10 and the pollutant variables have been taken from the meteorological station in Kuala Lumpur, Malaysia. All of these variables classified as nonlinear data. Logistic regression (LR) model can be used for modeling and forecasting these multivariable datasets. LR is one of linear statistical methods, therefore it may reflect inaccurate results when used with nonlinear datasets. To improve the results of forecasting, support vector machine (SVM) method has been suggested in this study. The results in this study reflect outperforming for SVM method comparing to LR. In conclusion, SVM forecasting can be used for more accuracy with nonlinear multivariate datasets when PM10 is as dependent variable.