Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand

Abstract

Open biomass burning (OBB) is the main cause of air pollution in Northern Thailand, where PM_2.5 concentrations exceed Thailand's air quality standards annually during the January–April (dry season). The air emissions from databases that detail the pollutants discharged into the atmosphere from specific sources of air pollution are crucial for monitoring air pollution. However, this data has been poorly studied in Thailand. This study estimated ground-level PM_2.5 concentration in Northern Thailand using the Multilayer Perceptron Artificial Neural Networks (MLP-ANN) model, integrating the in-depth data as input variables. The 10-fold cross-validation approach was applied to validate the model's performance. The meteorological and aerosol optical depth (AOD), which were the satellite data detected by a sensor from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites, were used as input variables. One important input variable was the OBB emission, which consists of forest fire and crop residue burning air emissions.  The best modeling was found with the optimal architecture networks, with 8-16-1 indicating the lowest mean absolute error (MAE) values at 0.0187 and root mean square error (RMSE) at 0.0282. The model result was observed as an underestimate between the model result and the actual data from the Pollution Control Department (PCD), for which the limitation of the training datasets was the reason. Moreover, this model was also applied to estimates in Khon Kaen, Nakhon Ratchasima, and Ubon Ratchathani provinces. The model results were still underestimated compared data from the PCD. The primary reasons were the difference in the geographical characteristics and air pollution, including the error model. The highlight finding in this study indicated that integrating the input variables as the meteorological data, the AOD, and the OBB emissions processing with MLP-ANN enhances model performance and can be extended to further estimation in areas without air monitoring stations.