PM<sub>2.5</sub>&thinsp;∕&thinsp;PM<sub>10</sub> ratio prediction based on a long short-term  memory neural network in Wuhan, China

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

doi:https://doi.org/10.5194/gmd-13-1499-2020

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

PM_2.5 ∕ PM₁₀ ratio prediction based on a long short-term memory neural network in Wuhan, China

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

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Final revised paper (published on 25 Mar 2020)
Preprint (discussion started on 05 Aug 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

SC1: 'Comments on "PM2.5 / PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China"', Zhexuan Liu, 04 Sep 2019
- RC1: 'PM2.5 / PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China', Anonymous Referee #1, 04 Sep 2019
  - AC1: 'We have revised the manuscript based on the comments. Please see the supplement for the revised manuscript.', Xueling Wu, 22 Nov 2019
- SC4: 'An excellent research, but need to make more endeavor', Kaixiang Zhang, 24 Sep 2019
SC2: 'Approved with minor edits', xujiao wang, 08 Sep 2019
SC3: 'An excellent research', Kaixiang Zhang, 23 Sep 2019
SC5: 'Executive editor comment, code and data availability.', David Ham, 25 Oct 2019
RC2: 'Review of PM2.5/PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network inWuhan, China by Wu et al.', Anonymous Referee #2, 11 Dec 2019
- AC2: 'response letter', Xueling Wu, 16 Dec 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Xueling Wu on behalf of the Authors (20 Dec 2019) Author's response

ED: Publish as is (20 Feb 2020) by David Topping

Short summary

This paper presents a composite prediction system designed to improve the accuracy and applicability of PM_2.5 / PM₁₀ predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM_2.5 / 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.

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

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Interactive discussion

Peer-review completion

PM_2.5 ∕ PM₁₀ ratio prediction based on a long short-term memory neural network in Wuhan, China