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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Cited articles

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Choi, Y.: The impact of satellite-adjusted NOmathitx emissions on simulated NOmathitx and O3 discrepancies in the urban and outflow areas of the Pacific and Lower Middle US, Atmos. Chem. Phys., 14, 675–690, https://doi.org/10.5194/acp-14-675-2014, 2014. 
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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.