Integrating D–S evidence theory and multiple deep learning frameworks for time series prediction of air quality
Abstract Accurate prediction of air quality time series data is helpful to identify and warn air pollution events in advance. Although the current air quality prediction models have made some progress in improving the accuracy of prediction, due to the impact of specific pollutants or complex meteor...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2025-02-01
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| Colecção: | Scientific Reports |
| Acesso em linha: | https://doi.org/10.1038/s41598-025-87935-3 |
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