Research on prediction of tropospheric NO2 column concentration based on machine learning

This paper combines the advantages of machine learning and satellite remote sensing to study a method for predicting the tropospheric NO₂ column concentration based on machine learning, so as to quickly and efficiently obtain the tropospheric NO₂ column concentration directly from satellite spectral data. Extreme gradient boosting, random forest and BP neural network algorithm models were constructed respectively. Among them, the BP neural network model has the best performance, with an average percentage error of 10.4% on the test set, a symmetric average absolute percentage error of 9.5%, and R² is 0.94, and the average percentage error is 1.8% and 12.1% lower than the random forest model and extreme gradient boosting model respectively. The optimal model was used to predict the daily tropospheric NO₂ column concentration and compared with TROPOMI's NO₂ product. The average percentage error was less than 10.3%, which fully demonstrates that the constructed BP neural network algorithm model can effectively predict the tropospheric NO₂ column concentration.