Predicting PM10 concentration using Bayesian regression with Non-Informative Prior and Conjugate Prior Model

Abstract

The aim of this study is to predict the next day PM10 concentration using Bayesian Regression with noninformative prior and conjugate prior models. The descriptive analysis of PM10, temperature, relative humidity, nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO) and ozone (O3) are also included. A case study used two-years of air quality monitoring data at three (3) monitoring stations to predict the future PM10 concentration with seven parameters (PM10, temperature, relative humidity, NO2, SO2, CO, and O3). The descriptive analysis showed that the highest mean PM10 concentration occurred at Klang station in 2011 (71.30 µg/m3) followed by 2012 (68.82 µg/m3). The highest mean PM10 concentration was at Nilai in 2012 (68.86 µg/m3) followed by 2011 (66.29µg/m3) respectively. The results showed that the Bayesian regression model used a conjugate prior with a normal-gamma prior which was a good model to predict the PM10 concentration for most study stations with (R2 = 0.67 at Jerantut station), (R2 = 0.61 at Nilai station) and (R2 = 0.66 at Klang station) respectively compared to a non-informative prior.