Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 9, issue 2
Articles | Volume 9, issue 2
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 2
22 Dec 2023
 | 22 Dec 2023

Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information

Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Santiago Hernández, and Mauricio Ramírez

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Latest update: 23 Nov 2024
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Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
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