Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6237-2020
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, Yunsoo Choi, Yannic Lops, Alqamah Sayeed, and Ahmed Khan Salman

Viewed

Total article views: 2,235 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,362 800 73 2,235 228 74 66
  • HTML: 1,362
  • PDF: 800
  • XML: 73
  • Total: 2,235
  • Supplement: 228
  • BibTeX: 74
  • EndNote: 66
Views and downloads (calculated since 09 Mar 2020)
Cumulative views and downloads (calculated since 09 Mar 2020)

Viewed (geographical distribution)

Total article views: 2,235 (including HTML, PDF, and XML) Thereof 1,931 with geography defined and 304 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.