Research on accurate prediction of Air quality index in Nanyang City driven by machine learning

Air quality prediction models often face the problems of insufficient generalization ability and poor prediction accuracy. To address this challenge, a machine learning model is proposed to be applied to Air Quality Index AQI prediction. Based on the air quality data of Nanyang City from 2023 to 2024, this study screened out PM2.5, PM10 and CO as key characteristic variables through correlation coefficient analysis. The parameters of eXtreme Gradient Boosting model, Random Forest model and Support Vector Regression model were optimized by using grid search method, and the model was trained and predicted. The experimental results show that the performance of SVR model optimized by grid search is the best. In terms of evaluation index, its R² is 0.886875, MAE is 0.026789, MSE is 0.001636. This study provides an efficient and feasible method for air quality prediction, and provides data support and technical reference for the formulation of air pollution prevention strategies and environmental management decisions.