Articles | Volume 6, issue 1
https://doi.org/10.5194/soil-6-215-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-6-215-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
03 Jun 2020
Original research article |
| 03 Jun 2020
| 03 Jun 2020
Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning
Anika Gebauer
CORRESPONDING AUTHOR
Department of Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
Monja Ellinger
Department of Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
Victor M. Brito Gomez
Departamento de Recursos Hídricos y Ciencias Ambientales,
Facultad de Ciencias Agropecuarias, Universidad de Cuenca, Cuenca, Ecuador
Mareike Ließ
Department of Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
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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.
Pedotransfer functions (PTFs) for soil water retention were developed for two tropical soil...
