Comparison of error and uncertainty of decision tree and learning vector quantization models for predicting soil classes in areas with low altitude variations

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Digital soil maps illustrate the spatial distribution of soil classes or properties and document the error and uncertainty of the soil class prediction. We assessed the potential of the decision tree (DT) and learning vector quantization (LVQ) models for prediction of soil classes in the Shahrekord plain (with low altitude variations), Iran, at different levels of Soil Taxonomy (ST) and World Reference Base (WRB) classification systems, and analyzed the error and uncertainty of both models. Two comprehensive datasets were used to predict the soil classes including soil characteristics derived from 120 excavated pedons using a stratified sampling scheme in the study area, and some auxiliary parameters (such as covariates of a digital elevation model). The cross-validation method was used to determine the uncertainty of the models. Results showed that the error and uncertainty of soil class prediction increased from the high levels towards lower levels in both soil classification systems. The first and second levels of the WRB system correlated with the suborder and subgroup levels of the ST system, respectively, which was also reflected in similar errors of these models for the predicted soil classes. The error and uncertainty in the LVQ model was remarkably higher than those of the DT model, proposing a higher accuracy of the DT model for prediction of soil classes in areas with low altitude variations. However, the LVQ model was demonstrated to be a more reliable model where the number of soil classes is low.