Machine Learning Approaches for PM2.5 Prediction: A Comparative Study

Air pollution poses a serious environmental and public health challenge, particularly due to fine particulate matter (PM₂. ₅), which can penetrate deep into the human respiratory system. Accurate forecasting of PM₂. ₅ concentrations is therefore essential for early warning systems and mitigation planning. This study presents a comparative evaluation of five predictive models—Linear Regression, Random Forest, XGBoost, CatBoost, and Long Short-Term Memory (LSTM) using a multi-year hourly (PM₂. ₅) dataset from India. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that all models achieve strong predictive performance, with LSTM yielding the lowest MAE and RMSE, while CatBoost attains the highest R². Visual analyses, including time-series comparisons and observed-versus-predicted plots, further validate model robustness. The findings demonstrate that machine learning and deep learning approaches can provide accurate and interpretable PM₂. ₅ forecasts, supporting effective air quality management and decision-making air quality forecasts to facilitate prompt decision-making.