Articles | Volume 6, issue 1
SOIL, 6, 215–229, 2020
https://doi.org/10.5194/soil-6-215-2020
SOIL, 6, 215–229, 2020
https://doi.org/10.5194/soil-6-215-2020

Original research article 03 Jun 2020

Original research article | 03 Jun 2020

Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning

Anika Gebauer et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Revision (25 Feb 2020) by Jan Vanderborght
AR by Anika Gebauer on behalf of the Authors (18 Mar 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (05 Apr 2020) by Jan Vanderborght
ED: Publish subject to technical corrections (06 Apr 2020) by Kristof Van Oost(Executive Editor)
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Short summary
Pedotransfer functions (PTFs) for soil water retention were developed for two tropical soil landscapes using machine learning. The models corresponding to these PTFs had to be adjusted by tuning their parameters. The standard tuning approach was compared to mathematical optimization. The latter resulted in much better model performance. The PTFs derived are of particular importance for soil process and hydrological models.