Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 21
Articles | Volume 29, issue 21
https://doi.org/10.5194/hess-29-6221-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6221-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 21
Research article
 | 
13 Nov 2025
Research article |  | 13 Nov 2025

How to deal w___ missing input data

Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Deborah Cohen, and Oren Gilon

Viewed

Total article views: 2,863 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,371 423 69 2,863 71 72
  • HTML: 2,371
  • PDF: 423
  • XML: 69
  • Total: 2,863
  • BibTeX: 71
  • EndNote: 72
Views and downloads (calculated since 07 Apr 2025)
Cumulative views and downloads (calculated since 07 Apr 2025)

Viewed (geographical distribution)

Total article views: 2,863 (including HTML, PDF, and XML) Thereof 2,842 with geography defined and 21 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2026
Download
Short summary
Missing input data are one of the most common challenges when building deep learning hydrological models. We present and analyze different methods that can produce predictions when certain inputs are missing during training or inference. Our proposed strategies provide high accuracy while allowing for more flexible data handling and being robust to outages in operational scenarios.
Read more
Share