Abstract:Air Quality Index (AQI) prediction can provide guidance for people’s daily production activities and air pollution control. In view of the problem that AQI prediction model is greatly affected by outliers, the isolation forest algorithm is used to detect outliers in the air quality data set; the Outlier Robust Extreme Learning Machine (ORELM) model is proposed for AQI prediction, and an error correction module is constructed to correct model prediction error. Finally, with the air quality data of Beijing as the research object for empirical analysis, the ORELM model and the Extreme Learning Machine (ELM) model are used to make predictions, and the prediction error of the ORELM model is corrected. Experimental results show that the ORELM has stronger generalization performance for outlier data sets, and the error correction module can effectively improve the prediction accuracy of the model.