Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2045-2021
https://doi.org/10.5194/hess-25-2045-2021
Research article
 | 
19 Apr 2021
Research article |  | 19 Apr 2021

Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network

Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter

Viewed

Total article views: 409,811 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
210,154 199,296 361 409,811 297 299
  • HTML: 210,154
  • PDF: 199,296
  • XML: 361
  • Total: 409,811
  • BibTeX: 297
  • EndNote: 299
Views and downloads (calculated since 17 Nov 2020)
Cumulative views and downloads (calculated since 17 Nov 2020)

Viewed (geographical distribution)

Total article views: 409,811 (including HTML, PDF, and XML) Thereof 370,667 with geography defined and 39,144 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 09 Sep 2026
Download
Short summary
We present multi-timescale Short-Term Memory (MTS-LSTM), a machine learning approach that predicts discharge at multiple timescales within one model. MTS-LSTM is significantly more accurate than the US National Water Model and computationally more efficient than an individual LSTM model per timescale. Further, MTS-LSTM can process different input variables at different timescales, which is important as the lead time of meteorological forecasts often depends on their temporal resolution.
Share