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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Cited articles

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Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., and Akanbi, L. A.: Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting, Machine Learning with Applications, 7, 100204, https://doi.org/10.1016/j.mlwa.2021.100204, 2022. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004. 
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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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