Forecasting air pollution index (API) PM2.5 using support vector machine (SVM) / Nor Hayati Shafii ... [et al.]

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index accurately is very important to control its impact on environmental and human health. The work presented here aims to access Air Pollution Index(API) of PM2.5accurately using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). SVM is relatively memory efficient and works relatively well in high dimensional spaces data which is better than the conventional method. The data used in this study is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the API based on the model testing with 0.03868583 (MAE) and 0.06251793 (RMSE) for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.