Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3707-2025
https://doi.org/10.5194/gmd-18-3707-2025
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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Cited articles

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W.: On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, PLOS ONE, 10, e0130140, https://doi.org/10.1371/journal.pone.0130140, 2015. a
Beckh, K., Müller, S., Jakobs, M., Toborek, V., Tan, H., Fischer, R., Welke, P., Houben, S., and von Rueden, L.: Explainable Machine Learning with Prior Knowledge: An Overview, arXiv [preprint], https://doi.org/10.48550/arXiv.2105.10172, 2021. a, b, c
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Bose, S., Hansel, N., Tonorezos, E. S., Williams, D. L., Bilderback, A., Breysse, P. N., Diette, G. B., and McCormack, M. C.: Indoor Particulate Matter Associated with Systemic Inflammation in COPD, J. Environ. Protect., 6, 566–572, https://doi.org/10.4236/jep.2015.65051, 2015. a
Ceamanos, X., Six, B., and Riedi, J.: Quasi-Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS-GEO, J. Geophys. Res.-Atmos., 126, e2021JD034906, https://doi.org/10.1029/2021JD034906, 2021. a
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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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