Preprints
https://doi.org/10.5194/egusphere-2023-2710
https://doi.org/10.5194/egusphere-2023-2710
10 Jan 2024
 | 10 Jan 2024

Combining Recurrent Neural Networks with Variational Mode Decomposition and Multifractals to Predict Rainfall Time Series

Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Abstract. Rainfall time series prediction is essential for monitoring urban hydrological systems, but it is challenging and complex due to the extreme variability of rainfall. A hybrid deep learning model (VMD-RNN) is used in order to improve prediction performance. In this study, variational mode decomposition (VMD) is first applied to decompose the original rainfall time series into several sub-sequences according to the frequency domain, where the number of decomposed sub-sequences is determined by power spectral density (PSD) analysis. To prevent the disclosure of forthcoming data, non-training time series are sequentially appended for generating the decomposed testing samples. Following that, different recurrent neural network (RNN) variant models are used to predict individual sub-sequences and the final prediction is reconstructed by summing the prediction results of sub-sequences. These RNN-variants are long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU), which are optimal for sequence prediction. In addition to three common evaluation criteria, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), the framework of universal multifractals (UM) is also introduced to assess the performance of predictions, which enables the extreme variability of predicted rainfall time series to be characterized. The study employs two rainfall time series with daily and hourly resolutions, respectively. The results indicate that the hybrid VMD-RNN model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure LSTM model without decomposition.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2710', Anonymous Referee #1, 26 Feb 2024
    • AC1: 'Reply on RC1', Hai Zhou, 11 Mar 2024
  • AC1: 'Reply on RC1', Hai Zhou, 11 Mar 2024
  • RC2: 'Comment on egusphere-2023-2710', Anonymous Referee #2, 06 Apr 2024
    • AC2: 'Reply on RC2', Hai Zhou, 01 May 2024
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Viewed

Total article views: 515 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
354 130 31 515 18 19
  • HTML: 354
  • PDF: 130
  • XML: 31
  • Total: 515
  • BibTeX: 18
  • EndNote: 19
Views and downloads (calculated since 10 Jan 2024)
Cumulative views and downloads (calculated since 10 Jan 2024)

Viewed (geographical distribution)

Total article views: 503 (including HTML, PDF, and XML) Thereof 503 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jul 2024
Download
Short summary
The hybrid VMD-RNN model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure LSTM model. The universal multifractals technique is also introduced to evaluate prediction performance, thus validating the usefulness and applicability of the hybrid model.