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Applied and Environmental Soil Science
Volume 2014, Article ID 603132, 10 pages
Research Article

The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions

1Department of Geosciences/Soil Physics Division, University of Bayreuth, Universitaetsstraße 30, 95447 Bayreuth, Germany
2ETH Zürich, Environmental Natural and Social Sciences, Universitaetsstraße 22, 8092 Zürich, Switzerland

Received 15 July 2013; Revised 12 October 2013; Accepted 28 October 2013; Published 5 February 2014

Academic Editor: Robert L. Bradley

Copyright © 2014 Mareike Ließ et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The sloping mire landscape of the investigation area, in the southern Andes of Ecuador, is dominated by stagnic soils with thick organic layers. The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. Terrain smoothing from 10 to 30 m raster resolution was applied in order to obtain the best possible model. For the same purpose, several model tuning parameters were tested and a prepredictor selection with the R-package Boruta was applied. Model versions were evaluated and compared by 100 repetitions of the calculation of the residual mean square error of a five-fold cross-validation. Position specific density functions of the predicted soil parameters were then used to display prediction uncertainty. Prepredictor selection and tuning of the Random Forest algorithm in some cases resulted in an improved model performance. We therefore recommend testing prepredictor selection and tuning to make sure that the best possible model is chosen. This needs particular emphasis in complex tropical mountain soil-landscapes which provide a real challenge to any soil mapping approach but where Random Forest has proven to be successful due to the testing of model tuning and prepredictor selection.