Which Model to Choose? Performance Comparison of Statistical and Machine Learning Models in Predicting PM2.5 From High-Resolution Satellite Aerosol Optical Depth

The mathematical solution to estimate surface fine particulate matter (PM2.5) from columnar aerosol optical depth (AOD) includes complex variables and involves a bunch of assumptions. Hence, researchers tend to use training-based models to predict PM2.5 from AOD. Here, we integrated regulatory composite PM2.5 measurements, high-resolution satellite AOD, reanalysis meteorological parameters, and a few other auxiliary parameters to train ten different regression models.