Combining variational mode decomposition and recurrent neural network to predict rainfall time series and evaluating prediction performance by universal multifractals.
- DOI
- 10.5194/egusphere-egu22-6014
- Published
- 2022-03-27
- Container
- Not recorded
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.5194/egusphere-egu22-6014,
title = {Combining variational mode decomposition and recurrent neural network to predict rainfall time series and evaluating prediction performance by universal multifractals.},
author = {Hai Zhou and Daniel Schertzer and Ioulia Tchiguirinskaia},
year = {2022},
doi = {10.5194/egusphere-egu22-6014},
url = {https://doi.org/10.5194/egusphere-egu22-6014}
}RIS
TY - JOUR TI - Combining variational mode decomposition and recurrent neural network to predict rainfall time series and evaluating prediction performance by universal multifractals. AU - Hai Zhou AU - Daniel Schertzer AU - Ioulia Tchiguirinskaia PY - 2022 DO - 10.5194/egusphere-egu22-6014 UR - https://doi.org/10.5194/egusphere-egu22-6014 ER -
APA
Zhou, H., Schertzer, D., & Tchiguirinskaia, I. (2022). Combining variational mode decomposition and recurrent neural network to predict rainfall time series and evaluating prediction performance by universal multifractals.. https://doi.org/10.5194/egusphere-egu22-6014
Source records
- hal · retrieved 2026-11-10T09:15:39.212Z