Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical t...

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Detalles Bibliográficos
Autores principales: Murad, Abdulmajid, Kraemer, Frank Alexander, Bach, Kerstin, Taylor, Gavin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659533/
https://www.ncbi.nlm.nih.gov/pubmed/34884011
http://dx.doi.org/10.3390/s21238009

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659533/
https://www.ncbi.nlm.nih.gov/pubmed/34884011
http://dx.doi.org/10.3390/s21238009

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