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Discussion papers
https://doi.org/10.5194/soil-2019-72
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-2019-72
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: original research article 04 Nov 2019

Submitted as: original research article | 04 Nov 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal SOIL (SOIL).

Development of pedotransfer functions for tropical mountain soilscapes: Spotlight on parameter tuning in machine learning

Anika Gebauer1, Monja Ellinger1, Victor M. Brito Gomez2, and Mareike Ließ1 Anika Gebauer et al.
  • 1Department Soil System Science, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany
  • 2Departamento de Recursos Hídricos y Ciencias Ambientales, Facultad de Ciencias Agropecuarias, Universidad de Cuenca, Cuenca, Ecuador

Abstract. Machine learning algorithms are good in computing non-linear problems and fitting complex composite functions, which makes them an adequate tool to address multiple environmental research questions. One important application is the development of pedotransfer functions (PTF). This study aims to develop water retention PTFs for two remote tropical mountain regions of rather different soil-landscapes, dominated by (1) organic soils under volcanic influence, and (2) tropical mineral soils. Two tuning procedures were compared to fit boosted regression tree models: (1) tuning by grid search, which is the standard approach in pedometrics and (2) tuning by differential evolution optimization. A nested cross-validation approach was applied to generate robust models. The developed area-specific PTFs outrival other more general PTFs. Furthermore, the first PTF for typical soils of Páramo landscapes, i.e. organic soils under volcanic influence, is presented. Overall, results confirmed the differential evolution algorithm’s high potential for tuning machine learning models. While models based on tuning by grid search roughly predicted the response variables' mean for both areas, models applying the differential evolution algorithm for parameter tuning explained up to 22 times more of the response variables' variance.

Anika Gebauer et al.
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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 optimisation. The latter resulted in much better model performance. The derived PTFs are of particular importance for soil process and hydrological models.
Pedotransfer functions (PTFs) for soil water retention were developed for two tropical...
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