APPLICATION OF STACKING IN GROUND-LEVEL PM<sub>2.5</sub> CONCENTRATION ESTIMATING

ZHAO Bin; LIU Bin

doi:10.13205/j.hjgc.202002022

Volume 38 Issue 2

Feb. 2020

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ZHAO Bin, LIU Bin. APPLICATION OF STACKING IN GROUND-LEVEL PM2.5 CONCENTRATION ESTIMATING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 153-159. doi: 10.13205/j.hjgc.202002022

Citation:

ZHAO Bin, LIU Bin. APPLICATION OF STACKING IN GROUND-LEVEL PM_2.5 CONCENTRATION ESTIMATING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 153-159. doi: 10.13205/j.hjgc.202002022

ZHAO Bin, LIU Bin. APPLICATION OF STACKING IN GROUND-LEVEL PM2.5 CONCENTRATION ESTIMATING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 153-159. doi: 10.13205/j.hjgc.202002022

Citation:

ZHAO Bin, LIU Bin. APPLICATION OF STACKING IN GROUND-LEVEL PM_2.5 CONCENTRATION ESTIMATING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 153-159. doi: 10.13205/j.hjgc.202002022

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APPLICATION OF STACKING IN GROUND-LEVEL PM_2.5 CONCENTRATION ESTIMATING

doi: 10.13205/j.hjgc.202002022

ZHAO Bin,
LIU Bin^,

School of Electrical Information & Artificial Intelligence, Shanxi University of Science and Technology, Xi'an 710021, China

Received Date: 2019-06-13

Abstract

Abstract

In order to solve the problem that the ground PM_2.5 measurement was limited in spatial and temporal coverage, satellite aerosol optical depth AOD with wide spatial-temporal coverage and stacking method were proposed to establish a PM_2.5 concentration estimation model. The AOD and meteorological parameter and PM_2.5 emissions related data were trained for building the model, and the improved grid search algorithm was used to optimize the hyperparameters of each model. Based on multi-collinearity analysis, the optimal PM_2.5 concentration estimation model based on Stacking was established. The data between January 1st, 2016 to December 31st, 2016, was selected as the experimental object. The experimental results showed that the performance of the stacking model using ridge regression as the meta-learner was better than that of the random forest, GBRT and XGBoost model. It was concluded that the stacking model was applicable for air pollution monitoring in a large geographical area.
- PM_2.5,
- air pollution,
- aerosol optical depth(AOD),
- ensemble learning,
- Stacking

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