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Applied and Environmental Soil Science
Volume 2014 (2014), Article ID 603132, 10 pages
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.
Citations to this Article [2 citations]
The following is the list of published articles that have cited the current article.
- Martin Hitziger, and Mareike Ließ, “Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes,” Applied and Environmental Soil Science, vol. 2014, pp. 1–12, 2014.
- Mareike Liess, Johannes Schmidt, and Bruno Glaser, “Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Le,” Plos One, vol. 11, no. 4, 2016.