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Remote sensing estimation of surface PM2.5 concentrations using a deep learning model improved by data augmentation and a particle size constraint by Shunchao Yin; Tongwen Li; Xiao Cheng; Jingan Wu is a Environmental Science article available to read on EtoBox.

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Accurate estimation of PM2.5 concentrations is critical to understanding and counteracting air pollution. In the past decade, various machine learning models, especially deep learning models, have been widely used in PM2.5 remote sensing estimation and have achieved remarkable performance. However, a typical pitfall of deep learning models is the problem of high-value underestimation, i.e., the models often underestimate high-level PM2.5 concentrations. Alleviating the problem of high-value underestimation and improving the estimation accuracy of high PM2.5 concentrations are crucial. This study developed a new deep learning model that combines data augmentation and a particle size constraint to improve the high-level PM2.5 estimation. Based on the residual neural network (ResNet), this study used random oversampling to construct a data-augmented deep residual learning model (AugResNet). In addition, a deep residual neural network model with a particle size constraint was established and called ConResNet. Then, the data augmentation and particle size constraint were incorporated into the deep residual neural network model (denoted as Aug_ConResNet). We evaluated the above four mode

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Author

Shunchao Yin; Tongwen Li; Xiao Cheng; Jingan Wu

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)