A spatiotemporal XGBoost model for PM(2.5) concentration prediction and its application in Shanghai

This paper innovatively constructed an analytical and forecasting framework to predict PM(2.5) concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, improvements are made on XGBoost prediction accuracy...

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Detalles Bibliográficos
Autores principales: Wang, Zidong, Wu, Xianhua, Wu, You
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696222/
http://dx.doi.org/10.1016/j.heliyon.2023.e22569

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696222/
http://dx.doi.org/10.1016/j.heliyon.2023.e22569

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