Comparative Analysis of Tree, Meta-learning and Function Classifiers to Predict the Atmospheric Concentration of NO2 - Masih, Adven (Journal of Environmental Accounting and Management, 2026)
The concentration of airborne pollutants is rising in recent years. Due to serious health effects of NO2, SO2 etc. their constant monitoring is important for the policy makers, as it provides early pollution estimates before it crosses permissible limits set by the state. For air quality modelling, several statistical techniques based on Artificial Neural Networks have been applied, however, Tree and meta-learning based classifiers have rarely been adopted for air pollution prediction purpose. Thus, for this study, Tree (Random Forest, Reduces Error Pruning (REP) Tree), meta-learning (Bagging, Random Subspace) and Function (Multilayer Perceptron and Support Vector Machine) based classifiers have been employed to predict atmospheric concentrations of Nitrogen dioxide (NO2). The study uses 3 atmospheric pollutants; Sulphur dioxide (SO2), Carbon monoxide (CO), and Hydrochloric acid (HCl) and 5 meteorological parameters temperature, humidity, wind speed, wind direction and atmospheric pressure. Moreover, for validation of prediction models the performance of different classifiers were compared. The results obtained suggest that Tree classifiers in general and Random Forest in particular, can outperform Function (MLP and SVM) and meta-learning (Bagging and Random Subspace) classifiers to predict the atmospheric concentration of NO2.