A Machine Learning-Based Model for 72-Hour Air Quality Prediction and Classification

Abstract

The core objective of this research is to construct a machine learning-based air quality prediction model. This model aims to forecast the Air Quality Index (AQI) for the next 72 hours and classify its corresponding levels (e.g., Good, Moderate, Polluted), providing a robust scientific basis for environmental protection departments and related decision-making. For feature selection, we analyzed multiple key factors affecting air quality. While meteorological data, spatiotemporal features, and external pollution sources are important, this study focuses on the historical concentrations of six critical pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) as model inputs to establish a baseline model, acknowledging the need for incorporating broader influencing factors in future work. In the model construction phase, we performed extensive preprocessing on the collected historical air quality data, including standardization and normalization, to extract effective information. We then employed and compared several advanced machine learning algorithms, selecting the optimal combination to build the final prediction model. The experiments were conducted using the Python language. By continuously optimizing model parameters and feature combinations, we achieved predictions for both the numerical AQI values and their corresponding quality levels for the subsequent 72 hours. Experimental results demonstrate that the constructed model possesses high prediction accuracy and stability for the predominant “Excellent” and “Good” categories. However, the lack of severe pollution events in the dataset limits the evaluation of its predictive capability for pollution peak events.