Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3555-2021
https://doi.org/10.5194/hess-25-3555-2021
Research article
 | 
23 Jun 2021
Research article |  | 23 Jun 2021

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

Yueling Ma, Carsten Montzka, Bagher Bayat, and Stefan Kollet

Viewed

Total article views: 2,337 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,652 634 51 2,337 53 58
  • HTML: 1,652
  • PDF: 634
  • XML: 51
  • Total: 2,337
  • BibTeX: 53
  • EndNote: 58
Views and downloads (calculated since 24 Aug 2020)
Cumulative views and downloads (calculated since 24 Aug 2020)

Viewed (geographical distribution)

Total article views: 2,337 (including HTML, PDF, and XML) Thereof 2,147 with geography defined and 190 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) data from integrated hydrologic simulation results over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.