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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| 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 |