Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7411-2021
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, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki

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Cited articles

Adams, M. D. and Kanaroglou, P. S.: Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models, J. Environ. Manag., 168, 133–141, https://doi.org/10.1016/j.jenvman.2015.12.012, 2016. a, b, c
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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.