Geoscientific Model Development
Geoscientific Model Development
Geoscientific Model Development
Articles | Volume 13, issue 3
Articles | Volume 13, issue 3
Geosci. Model Dev., 13, 1499–1511, 2020
https://doi.org/10.5194/gmd-13-1499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 1499–1511, 2020
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 et al.

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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