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.

Data sets

DATA and PTFs: Development of pedotransfer functions for water retention in tropical mountain soilscapes: Spotlight on parameter tuning in machine learning M. Ließ, A. Gebauer, M. Ellinger, V. M. Brito Gomez, and C. M. Guio Blanco https://doi.org/10.17605/OSF.IO/7UBWY

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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.