Analyzing the Performance Criteria of ARMA Model for Air Quality Forecasting in Jakarta - ALEXANDRA, Christina; TANTRI, Tiffany; GUNAWAN, Fergyanto E. (Journal of Environmental Management and Tourism, 2020)
Air pollution has been receiving global attention because of its effects on human health and the environment. The ability to predict the level of a pollutant concentration is important for various purposes such as for prevention and mitigation. Some papers had implemented various forecasting techniques to predict air pollution in Mexico, Spain, Malaysia. This research intends to investigate the forecasting accuracy of some air pollutants (PM10, SO2, CO, O3, and NO2) in Jakarta, Indonesia. The data are acquired daily for five measurement sites across the city, namely, Bundaran HI, Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk during January-October of 2018. The data are fitted with Auto-Regressive Moving-Average (ARMA) models. The best model is obtained by comparing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) with the relative error. The best and the most accurate model is the model with the lowest value of AIC or BIC.