Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 23, issue 11
Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 11
Research article
 | 
15 Nov 2019
Research article |  | 15 Nov 2019

Modelling of the shallow water table at high spatial resolution using random forests

Julian Koch, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg

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Short summary
This study explores novel modelling avenues using machine learning in combination with process-based models to predict the shallow water table at high spatial resolution. Due to climate change and anthropogenic impacts, the shallow groundwater is rising in many parts of the world. In order to adapt to risks induced by groundwater flooding, new modelling tools need to emerge. In this study, we found that machine learning is capable of reaching the required accuracy and resolution.
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