基于离群点检测和误差修正的空气质量指数预测

Air Quality Index Prediction Based on Outlier Detection and Error Correction

空气质量指数(Air Quality Index, AQI)预测可以为人们日常生产活动以及空气污染治理工作提供指导.针对空气质量指数预测模型受离群点影响较大的问题,利用孤立森林算法对空气质量数据集进行离群点分析,采用离群鲁棒极限学习机模型(ORELM)对空气质量指数进行预测,并构建误差修正模块对模型预测误差进行修正.最后,以北京市空气质量数据作为研究对象,分别利用ORELM模型以及极限学习机(ELM)模型进行预测,并对ORELM模型预测结果进行误差修正.实验结果表明:离群鲁棒极限学习机对离群点数据集泛化性能更强,误差修正模块能有效提高模型的预测精度.

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.