Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, government envisions a path to environmental sustainability and an improvement air quality. Air quality measurements were initiated different backgrounds including urban, suburban, industrial rural detect any significant changes parameters. Due dynamic nature of weather, geographical location anthropogenic sources, many uncertainties must be considered when dealing with pollution data. recent years, Bayesian approach fitting statistical models has gained more popularity due its alternative modelling strategy that accounted all Therefore, this study aims evaluate performance Model Averaging (BMA) predicting next-day PM10 concentration Peninsular Malaysia. A case utilized seventeen years’ worth monitoring data from nine (9) stations located using eight parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity wind speed. The performances prediction calculated five models’ evaluators, namely Coefficient Determination (R2), Index Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Squared (RMSE) Percentage (MAPE). BMA indicate humidity, speed contributed most model majority (R2 = 0.752 at Pasir Gudang station), 0.749 Larkin 0.703 Kota Bharu 0.696 Kangar station) 0.692 Jerantut respectively. Furthermore, demonstrated good performance, IA ranging 0.84 0.91, R2 0.64 0.75 KGE 0.61 0.74 stations. According results investigation, should utilised research forecasting operations pertaining issues such as pollution. From study, is recommended one tools concentration, especially particulate matter level.