Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data

[Image: see text] Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm “correct and smooth”. T...

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Detalles Bibliográficos
Autores principales: Betancourt, Clara, Li, Cathy W. Y., Kleinert, Felix, Schultz, Martin G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666531/
https://www.ncbi.nlm.nih.gov/pubmed/37661931
http://dx.doi.org/10.1021/acs.est.3c05104

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666531/
https://www.ncbi.nlm.nih.gov/pubmed/37661931
http://dx.doi.org/10.1021/acs.est.3c05104

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