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7 August 2024 Research on prediction of tropospheric NO2 column concentration based on machine learning
Mengyuan Wang, Fuqi Si, Runze Song
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Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132290I (2024) https://doi.org/10.1117/12.3038196
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Mengyuan Wang, Fuqi Si, and Runze Song "Research on prediction of tropospheric NO2 column concentration based on machine learning", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132290I (7 August 2024); https://doi.org/10.1117/12.3038196
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KEYWORDS
Neural networks
Data modeling
Machine learning
Satellites
Atmospheric modeling
Random forests
Nitrogen dioxide
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