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.

数字土壤图显示了土壤类别或特性的空间分布，并记录了土壤类别预测的误差和不确定性。我们评估了决策树（DT）和学习矢量量化（LVQ）模型在不同水平的土壤分类法（ST）和世界参考基准地区的伊朗Shahrekord平原（低海拔变化）中土壤类别的预测潜力（WRB）分类系统，并分析了两个模型的误差和不确定性。使用两个综合数据集来预测土壤类别，其中包括在研究区域中使用分层采样方案从120个挖掘的脚蹬中得出的土壤特征以及一些辅助参数（例如数字高程模型的协变量）。交叉验证方法用于确定模型的不确定性。结果表明，在两种土壤分类系统中，土壤分类预测的误差和不确定性都从高水平向低水平增加。WRB系统的第一级和第二级分别与ST系统的子级和子组级相关，这也反映在这些模型对预测土壤类别的类似误差中。LVQ模型中的误差和不确定性显着高于DT模型，这表明DT模型在预测低海拔变化地区的土壤类别时具有更高的准确性。但是，LVQ模型被证明是一种更可靠的模型，其中土壤类别数量很少。WRB系统的第一级和第二级分别与ST系统的子级和子组级相关，这也反映在这些模型对预测土壤类别的类似误差中。LVQ模型中的误差和不确定性显着高于DT模型，这表明DT模型在预测低海拔变化地区的土壤类别时具有更高的准确性。但是，LVQ模型被证明是一种更可靠的模型，其中土壤类别数量很少。WRB系统的第一级和第二级分别与ST系统的子级和子组级相关，这也反映在这些模型对预测土壤类别的类似误差中。LVQ模型中的误差和不确定性显着高于DT模型，这表明DT模型在预测低海拔变化地区的土壤类别时具有更高的准确性。但是，LVQ模型被证明是一种更可靠的模型，其中土壤类别数量很少。提出了更高的DT模型精度，以预测低海拔变化地区的土壤类别。但是，LVQ模型被证明是一种更可靠的模型，其中土壤类别数量很少。提出了更高的DT模型精度，以预测低海拔变化地区的土壤类别。但是，LVQ模型被证明是一种更可靠的模型，其中土壤类别数量很少。