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...