Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 23, issue 12
Articles | Volume 23, issue 12
https://doi.org/10.5194/hess-23-5089-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-5089-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 12
Research article
 | 
17 Dec 2019
Research article |  | 17 Dec 2019

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, and Grey Nearing

Viewed

Total article views: 427,081 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
222,654 204,120 307 427,081 466 403
  • HTML: 222,654
  • PDF: 204,120
  • XML: 307
  • Total: 427,081
  • BibTeX: 466
  • EndNote: 403
Views and downloads (calculated since 02 Aug 2019)
Cumulative views and downloads (calculated since 02 Aug 2019)

Viewed (geographical distribution)

Total article views: 427,081 (including HTML, PDF, and XML) Thereof 385,630 with geography defined and 41,451 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Dec 2025
Download
Short summary
A new approach for regional rainfall–runoff modeling using long short-term memory (LSTM)-based models is presented and benchmarked against a range of well-known hydrological models. The approach significantly outperforms regionally calibrated hydrological models but also basin-wise calibrated models. Furthermore, we propose an adaption of the LSTM that allows us to extract the learned catchment understanding of the model and show that it matches our hydrology expert knowledge.
Read more
Share