Performance Comparison of Malaysian Air Pollution Index Prediction Using Nonlinear Autoregressive Exogenous Artificial Neural Network and Support Vector Machine

This paper compares the performance of Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Machine (SVM) regression model to predict Air Pollutant Index (API) in Malaysia. Two models namely NARX SVM were developed using API air quality time series data from three monitoring stations: Pasir Gudang, TTDI Jaya Larkin. Hourly parameters collected year 2016 2018 utilized produce one step ahead prediction. The consist NO2, SO2, CO, O3, PM2.5, PM10 concentration as well meteorological which are wind speed, direction ambient temperature. was realized a series-parallel feed-forward network. For model, different kernel functions: Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian Coarse evaluated. measured Root Mean Square Error (RMSE) Coefficient Determination (R 2 ) values. Results show that outperformed both respectively.