PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network

As the core of pollution prevention and management, accurate PM2.5 concentration prediction is crucial for human survival. However, due to the nonstationarity and nonlinearity of PM2.5 concentration data, the accurate prediction for PM2.5 concentration remains a challenge. In this study, a PM2.5 con...

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Detalles Bibliográficos
Autores principales: Zhang, Li, Liu, Jinlan, Feng, Yuhan, Wu, Peng, He, Pengkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202073/
https://www.ncbi.nlm.nih.gov/pubmed/37213020
http://dx.doi.org/10.1007/s11356-023-27630-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202073/
https://www.ncbi.nlm.nih.gov/pubmed/37213020
http://dx.doi.org/10.1007/s11356-023-27630-w

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