Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations
As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor va...
Main Authors: | , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2024-01-01
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Series: | Geoscience Frontiers |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987123001536 |