Cargando…
Short period PM(2.5) prediction based on multivariate linear regression model
A multivariate linear regression model was proposed to achieve short period prediction of PM(2.5) (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological f...
Autores principales: | Zhao, Rui, Gu, Xinxin, Xue, Bing, Zhang, Jianqiang, Ren, Wanxia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062037/ https://www.ncbi.nlm.nih.gov/pubmed/30048475 http://dx.doi.org/10.1371/journal.pone.0201011 |
Ejemplares similares
-
PM(2.5) Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression
por: Liaw, Jiun-Jian, et al.
Publicado: (2020) -
Spatial distribution characteristics of PM(2.5) and PM(10) in Xi’an City predicted by land use regression models
por: Han, Li, et al.
Publicado: (2020) -
Nucleophilicity Prediction via Multivariate
Linear Regression Analysis
por: Orlandi, Manuel, et al.
Publicado: (2021) -
Predictive and mechanistic multivariate linear regression models for reaction development
por: Santiago, Celine B., et al.
Publicado: (2018) -
Predicting relative efficiency of amide bond formation using multivariate linear regression
por: Haas, Brittany C., et al.
Publicado: (2022)