Articles | Volume 13, issue 12
Geosci. Model Dev., 13, 6237–6251, 2020
https://doi.org/10.5194/gmd-13-6237-2020
Geosci. Model Dev., 13, 6237–6251, 2020
https://doi.org/10.5194/gmd-13-6237-2020

Model evaluation paper 09 Dec 2020

Model evaluation paper | 09 Dec 2020

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Ebrahim Eslami et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (19 Jun 2020)  Author's response
ED: Referee Nomination & Report Request started (20 Aug 2020) by Adrian Sandu
RR by Anonymous Referee #1 (07 Sep 2020)
RR by Anonymous Referee #3 (08 Sep 2020)
ED: Publish subject to minor revisions (review by editor) (02 Oct 2020) by Adrian Sandu
AR by Lorena Grabowski on behalf of the Authors (13 Oct 2020)  Author's response
ED: Publish as is (25 Oct 2020) by Adrian Sandu
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Short summary
As using deep learning algorithms has become a popular data analytic technique, atmospheric scientists should have a balanced perception of their strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network.