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

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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.
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