Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 12
Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3707-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-3707-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 12
Development and technical paper
 | 
23 Jun 2025
Development and technical paper |  | 23 Jun 2025

Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network

Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat

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Short summary
This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
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