Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 3
Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 3
Model evaluation paper
 | 
25 Mar 2020
Model evaluation paper |  | 25 Mar 2020

PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China

Xueling Wu, Ying Wang, Siyuan He, and Zhongfang Wu

Data sets

PM2.5/PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China Xueling Wu https://doi.org/10.17632/zk9k53zw3z.3

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Short summary
This paper presents a composite prediction system designed to improve the accuracy and applicability of PM2.5 / PM10 predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM2.5 / PM10-related factors from meteorological factors and air pollutants and compared the effects of several intelligent models in different prediction patterns. The results showed that the LSTM model had significant advantages in accuracy and stability.
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