Ozone prediction based on support vector machine - M. Tanaskuli; Ali N. Ahmed; Nuratiah Zaini; Samsuri Abdullah; Abdoulhdi A. Borhana; N. A. Mardhiah; Mathivanan Mathivanan (Indonesian Journal of Electrical Engineering and Computer Science, 2020)
The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model.