FC-MIDTR-WCCA: A Machine Learning Framework for PM2.5 Prediction

The rapid acceleration of urbanization and industrialization has led to a significant increase in PM2.5 pollution, making it a critical global concern. The accurate prediction of PM2.5 concentrations is of utmost importance for the effective implementation of protective measures and environmental management. This study presents a machine learning framework for PM2.5 prediction called FC-MIDTR-WCCA. The framework is composed of three main components. The first component involves conducting an analysis of air quality PM2.5 data to identify features highly correlated with PM2.5 and to examine seasonal patterns. This approach facilitates feature crossing (FC) by combining different relevant features. The second component utilizes a feature selection algorithm known as the mutual information decision tree regressor (MIDTR) to effectively account for correlations and contributions among features. This algorithm identifies the optimal feature dataset. The third component involves the adoption of a weighted arithmetic mean fusion algorithm that combines canonical correlation analysis (WCCA) for PM2.5 prediction. This algorithm considers the correlations between prediction models and addresses collinearity issues to achieve stable model weight vectors. We experimentally assessed the performance of four ensemble tree models and the stacking algorithm. The results demonstrated that the FC-MIDTR-WCCA model outperformed all the other methods evaluated in terms of R2 and MAE. © (2024), (International Association of Engineers). All Rights Reserved.