Articles | Volume 14, issue 12
Geosci. Model Dev., 14, 7411–7424, 2021
https://doi.org/10.5194/gmd-14-7411-2021
Geosci. Model Dev., 14, 7411–7424, 2021
https://doi.org/10.5194/gmd-14-7411-2021

Model description paper 02 Dec 2021

Model description paper | 02 Dec 2021

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Moritz Lange et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Kai Puolamäki on behalf of the Authors (14 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (05 Oct 2021) by Adrian Sandu
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Short summary
This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.