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

Related authors

The source, quantity, and spatial distribution of interfacial water during glide-snow avalanche release: experimental evidence from field monitoring

Amelie Fees, Michael Lombardo, Alec van Herwijnen, Peter Lehmann, and Jürg Schweizer

The Cryosphere, 19, 1453–1468, https://doi.org/10.5194/tc-19-1453-2025,https://doi.org/10.5194/tc-19-1453-2025, 2025

Short summary

Quantification of capillary rise dynamics in snow using neutron radiography

Michael Lombardo, Amelie Fees, Anders Kaestner, Alec van Herwijnen, Jürg Schweizer, and Peter Lehmann

EGUsphere, https://doi.org/10.5194/egusphere-2025-304,https://doi.org/10.5194/egusphere-2025-304, 2025

Short summary

Glide-snow avalanches: a mechanical, threshold-based release area model

Amelie Fees, Alec van Herwijnen, Michael Lombardo, Jürg Schweizer, and Peter Lehmann

Nat. Hazards Earth Syst. Sci., 24, 3387–3400, https://doi.org/10.5194/nhess-24-3387-2024,https://doi.org/10.5194/nhess-24-3387-2024, 2024

Short summary

Hydro-pedotransfer functions: a roadmap for future development

Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti

Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024,https://doi.org/10.5194/hess-28-3391-2024, 2024

Short summary

Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland

Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli

Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023,https://doi.org/10.5194/nhess-23-1059-2023, 2023

Short summary

Cited articles

Achieng, K. O.: Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models, Comput. Geosci., 133, 104320, https://doi.org/10.1016/j.cageo.2019.104320, 2019.

Aqel, N.: Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks-Dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.10600669, 2024a.

Aqel, N.: Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks – python codes. Zenodo [code], https://doi.org/10.5281/zenodo.10602397, 2024b.

Aqel, N.: Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks – Autoencoder part, Zenodo [code], https://doi.org/10.5281/zenodo.10605108, 2024c.

Basile, A., Bonfante, A., Coppola, A., De Mascellis, R., Falanga Bolognesi, S., Terribile, F., and Manna, P.: How does PTF Interpret Soil Heterogeneity? A Stochastic Approach Applied to a Case Study on Maize in Northern Italy, Water (Basel), 11, 275, https://doi.org/10.3390/w11020275, 2019.