Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM<sub>2.5</sub> Components

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM<sub>2.5</sub> Components

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in pr...

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Bibliographic Details
Main Authors: Tianyu Zhang, Guannan Geng, Yang Liu, Howard H. Chang
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Atmosphere
Subjects:
regression trees
machine learning
Bayesian model
particulate matter
Community Multiscale Air Quality (CMAQ)
aerosol optical depth
Online Access:https://www.mdpi.com/2073-4433/11/11/1233

Internet

https://www.mdpi.com/2073-4433/11/11/1233

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