Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 18
Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4437-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4437-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 18
Research article
 | 
17 Sep 2025
Research article |  | 17 Sep 2025

Combining recurrent neural networks with variational mode decomposition and multifractals to predict rainfall time series

Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Viewed

Total article views: 2,983 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,434 469 80 2,983 73 83
  • HTML: 2,434
  • PDF: 469
  • XML: 80
  • Total: 2,983
  • BibTeX: 73
  • EndNote: 83
Views and downloads (calculated since 10 Jan 2024)
Cumulative views and downloads (calculated since 10 Jan 2024)

Viewed (geographical distribution)

Total article views: 2,983 (including HTML, PDF, and XML) Thereof 2,983 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jan 2026
Download
Short summary
The hybrid variational mode decomposition–recurrent neural network (VMD-RNN) model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure long short-term memory (LSTM) model. The universal multifractal technique is also introduced to evaluate prediction performance, thus validating the usefulness and applicability of the hybrid model.
Read more
Share