Articles | Volume 13, issue 12
Geosci. Model Dev., 13, 6237–6251, 2020
Geosci. Model Dev., 13, 6237–6251, 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

Aiazzi, B., Alparone, L., Baronti, S., and Garzelli, A.: Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis, IEEE T. Geosci. Remote, 40, 2300–2312, 2002. 
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Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., and Blaschke, T.: The rise of deep learning in drug discovery, Drug Discov. Today, 23, 1241–1250, 2018. 
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,, 2014. 
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.