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

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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. 
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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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