Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 18
Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-6949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 18
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

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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.
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