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

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

doi:https://doi.org/10.5194/gmd-17-6949-2024

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

Data sets

Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks-Dataset Nedal Aqel https://doi.org/10.5281/zenodo.10600669

Model code and software

Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks- python codes Nedal Aqel https://doi.org/10.5281/zenodo.10602397

Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks- Autoencoder part Nedal Aqel https://doi.org/10.5281/zenodo.10605108

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.