Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2685-2021
https://doi.org/10.5194/hess-25-2685-2021
Research article
 | 
20 May 2021
Research article |  | 20 May 2021

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing

Viewed

Total article views: 6,199 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,304 1,803 92 6,199 106 97
  • HTML: 4,304
  • PDF: 1,803
  • XML: 92
  • Total: 6,199
  • BibTeX: 106
  • EndNote: 97
Views and downloads (calculated since 14 May 2020)
Cumulative views and downloads (calculated since 14 May 2020)

Viewed (geographical distribution)

Total article views: 6,199 (including HTML, PDF, and XML) Thereof 5,760 with geography defined and 439 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Dec 2024
Download
Short summary
We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.