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 μg/m3, the R and RMSE values for decision tree were 0.854 and 31.53 μg/m3, respectively, while the evaluation coefficient of SVM algorithm was only 0.597 and 49.31 μg/m3, 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.

中文翻译：

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使用 Himawari-8 气溶胶光学深度估计 PM2.5 浓度的六种机器学习方法的比较

基于卫星遥感的 PM2.5 浓度估计是目前研究的热点，机器学习算法开始应用于估计模型。然而，不同机器学习算法之间的比较很少，也很少有学者客观评价机器学习算法在不同天气条件下的估计性能。在这项研究中，PM2.5 浓度通过六种常用的机器学习算法进行估计，并在京津冀 (BTH) 地区的四种不同天气条件下比较它们的性能。结果表明，决策树和随机森林在不同的天气条件下始终表现良好，而 SVM 表现不佳。当 PM2.5 浓度大于 150 μg/m3 时，决策树的 R 和 RMSE 值分别为 0.854 和 31.53 μg/m3，而 SVM 算法的评价系数仅为 0.597 和 49.31 μg/m3，值得注意的是，所有算法在该区间的表现都优于其他算法。本研究还重点开发了一种优化组合算法来估算不同天气条件下的PM2.5浓度，并取得了良好的应用效果。本研究结果可为机器学习算法在遥感领域的应用提供理论依据和重要参考。本研究还重点开发了一种优化组合算法来估算不同天气条件下的PM2.5浓度，并取得了良好的应用效果。本研究结果可为机器学习算法在遥感领域的应用提供理论依据和重要参考。本研究还重点开发了一种优化组合算法来估算不同天气条件下的PM2.5浓度，并取得了良好的应用效果。本研究结果可为机器学习算法在遥感领域的应用提供理论依据和重要参考。