Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

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Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “About 80% of the case studies used at least one tree-based algorithm such as regression tree (e.g. Taghizadeh-Mehrjardi et al., 2019b; Heung et al., 2016), RF (e.g. Häring et al., 2012) or boosted regression tree (e.g. Lorenzetti et al., 2015).”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “This is chosen as an important factor when modeling [66].”
Predictive soil parent material mapping at a regional-scale: a Random Forest approach. (2014, result): “In contrast to the polygon disaggregation study in Häring et al. (2012), this study did not constrain the number of different parent material classes to the ones identified by the multi-component polygons in order to account for the inclusion of parent materials that were not recognized.”
Digital soil mapping: A brief history and some lessons (2016, background): “, 2014), or disaggregating the soil mapping units into soil series (Häring et al., 2012; Subburayalu and Slater, 2013; Odgers et al., 2014).”