Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-6949-2024
https://doi.org/10.5194/gmd-17-6949-2024
Model description paper
 | 
18 Sep 2024
Model description paper |  | 18 Sep 2024

Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks

Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann

Viewed

Total article views: 1,017 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
546 127 344 1,017 29 25
  • HTML: 546
  • PDF: 127
  • XML: 344
  • Total: 1,017
  • BibTeX: 29
  • EndNote: 25
Views and downloads (calculated since 04 Mar 2024)
Cumulative views and downloads (calculated since 04 Mar 2024)

Viewed (geographical distribution)

Total article views: 1,017 (including HTML, PDF, and XML) Thereof 1,014 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Dec 2024
Download
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.