Dataset Information

Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations.

ABSTRACT:

Background

Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5μm) 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 PM2.5 concentrations.

Methods

An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily PM2.5 concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of PM2.5 concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate PM2.5 concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for PM2.5 estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily PM2.5 at each 1km×1km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)].

Results

The results revealed that the PM2.5 prediction performance of DEML [coefficients of determination (R2)=0.87 and root mean square error (RMSE)=5.38μg/m3] was superior to any benchmark models (with R2 of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of PM2.5 in Italy.

Discussion

The proposed DEML framework achieved an outstanding performance in PM2.5 estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752.

SUBMITTER: Yu W

PROVIDER: S-EPMC8901043 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations.

Yu Wenhua W Li Shanshan S Ye Tingting T Xu Rongbin R Song Jiangning J Guo Yuming Y

Environmental health perspectives 20220307 3

<h4>Background</h4>Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5μm) is critical and essential for environmental health risk assessment.<h4>Objectives</h4>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 PM2.5 concentrations.<h4>Methods</h4>An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily P ...[more]

PMID: 35254864

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Dataset Information

Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations.

Background

Objectives

Methods

Results

Discussion

Publications

Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations.

Similar Datasets

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