Deep Ensemble Machine Learning Framework for the Estimation of Concentrations
Background: Accurate estimation of historical PM 2.5 (particle matter with an aerodynamic
diameter of less than 2.5 μ m) is critical and essential for environmental health risk
assessment. Objectives: The aim of this study was to develop a multiple-level stacked
ensemble machine learning framework for improving the estimation of the daily ground-level
PM 2.5 concentrations. Methods: An innovative deep ensemble machine learning framework
(DEML) was developed to estimate the daily PM 2.5 concentrations. The framework has a …
diameter of less than 2.5 μ m) is critical and essential for environmental health risk
assessment. Objectives: The aim of this study was to develop a multiple-level stacked
ensemble machine learning framework for improving the estimation of the daily ground-level
PM 2.5 concentrations. Methods: An innovative deep ensemble machine learning framework
(DEML) was developed to estimate the daily PM 2.5 concentrations. The framework has a …
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