Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4603-2019
https://doi.org/10.5194/hess-23-4603-2019
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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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (26 Aug 2019) by Graham Fogg
AR by Julian Koch on behalf of the Authors (11 Sep 2019)  Author's response   Manuscript 
ED: Publish as is (08 Oct 2019) by Graham Fogg
AR by Julian Koch on behalf of the Authors (08 Oct 2019)  Manuscript 
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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.