Research Article
PM2.5 Concentration Prediction Based on mRMR-XGBoost Model
@INPROCEEDINGS{10.1007/978-3-031-04409-0_30, author={Weijian Zhong and Xiaoqin Lian and Chao Gao and Xiang Chen and Hongzhou Tan}, title={PM2.5 Concentration Prediction Based on mRMR-XGBoost Model}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={PM2.5 Feature selection mRMR XGBoost Concentration prediction}, doi={10.1007/978-3-031-04409-0_30} }- Weijian Zhong
Xiaoqin Lian
Chao Gao
Xiang Chen
Hongzhou Tan
Year: 2022
PM2.5 Concentration Prediction Based on mRMR-XGBoost Model
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_30
Abstract
Air pollution is one of the main environmental pollution, in which air pollution component prediction is an important problem. At present, there have been many studies using machine learning methods to predict air pollution components. However, due to its numerous influencing factors and incomplete determination, there are still problems in accurate prediction. In this paper, the gas factors and meteorological factors collected by the self-developed integrated system are firstly used to construct the original feature set. Then, the mRMR algorithm is used to select data features from the perspective of maximum correlation and minimum redundancy. Finally, a prediction method of PM2.5 concentration in the next hour based on feature selection and XGBoost is designed by combining the data after dimension reduction with the XGBoost model. The experimental results show that mRMR algorithm can effectively select the features of air, and the prediction accuracy is improved even when only half of the features of the original data are used.
