Comparison of Six Machine Learning Methods for Estimating PM2.5 Concentration Using the Himawari-8 Aerosol Optical Depth

The estimation of the PM2.5 concentration based on satellite remote sensing is currently a hot topic for research, with machine learning algorithms starting to be applied to the estimation model. However, there are few comparisons between different machine learning algorithms, and few scholars objectively evaluate the estimation performance of machine learning algorithms under different weather conditions. In this study, PM2.5 concentration was estimated by six commonly used machine learning algorithms, and their performances were compared in four different weather conditions in the Beijing-Tianjin-Hebei (BTH) region. The results showed that decision tree and random forest consistently performed well in different weather conditions, while SVM performed poorly. When the PM2.5 concentration was greater than 150 mu g/m(3), the R and RMSE values for decision tree were 0.854 and 31.53 mu g/m(3), respectively, while the evaluation coefficient of SVM algorithm was only 0.597 and 49.31 mu g/m(3), it was worth noting that all algorithms performed better in this interval than in others. This study also focused on the development of an optimal combination algorithm to estimate PM2.5 concentration under different weather conditions and got a good application effect. The results of this research may provide a theoretical basis and an important reference for the application of machine learning algorithms in the field of remote sensing.