Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-1209-2019
https://doi.org/10.5194/gmd-12-1209-2019
Development and technical paper
 | 
29 Mar 2019
Development and technical paper |  | 29 Mar 2019

Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

Christoph A. Keller and Mat J. Evans

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Latest update: 27 Mar 2024
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Short summary
Computer simulations of atmospheric chemistry are a central tool to study the impact of air pollutants on the environment. These models are highly complex and require a lot of computing resources. In this study we show that machine learning can be used to predict air pollution with an accuracy that is comparable to the traditional, computationally expensive method. Such a machine-learning-based model has the potential to be orders of magnitude faster.