Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 21
Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 21
Technical note
 | 
04 Nov 2022
Technical note |  | 04 Nov 2022

Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks

Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo

Viewed

Total article views: 2,441 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,694 714 33 2,441 25 23
  • HTML: 1,694
  • PDF: 714
  • XML: 33
  • Total: 2,441
  • BibTeX: 25
  • EndNote: 23
Views and downloads (calculated since 25 Oct 2021)
Cumulative views and downloads (calculated since 25 Oct 2021)

Viewed (geographical distribution)

Total article views: 2,441 (including HTML, PDF, and XML) Thereof 2,319 with geography defined and 122 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Jun 2023
Download
Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
Read more