خلاصة:
Soil classification systems are very useful for a simple and fast summarization of soil properties. These systems indicate the method for data summarization and facilitate connections among researchers, engineers, and other users. One of the practical systems for soil classification is Soil Taxonomy (ST). As determining soil classes for an entire area is expensive, time-consuming, and almost impossible, this research has tried to predict the soil classes in each level of the ST system (up to family level) by using the data of 120 excavated pedons and some auxiliary parameters (such as derivatives of digital elevation model, i.e., DEM) in Shahrekord plain, central Iran. For this reason, the decision tree model was encoded and implemented in the MATLAB software for three conditions: use of soil properties, auxiliary parameters, and its combination. According to the results, soil class prediction error by using soil properties, auxiliary parameters, and its combination was estimated to be 0, 3.33 and 0% for order and suborder levels; 0.83, 15 and 0.83% for great group level; 3.33, 22.5 and 3.33% for subgroup level and 30, 52.5 and 30% for family level, respectively. In addition, the use of kriging maps of soil properties (instead of 120 observational points) decreased the prediction error of the modeling in all levels of the ST system. It seems that the effect of auxiliary parameters (in comparison to soil properties) is not very significant for predicting soil classes in low-relief areas.
ملخص الجهاز:
Therefore, the main goal of the present research is to use soil properties, auxiliary parameters, its combination in predicting soil classes, and comparing the results at different levels of the ST system (up to family class) using the decision tree model.
In addition, some properties including the presence or absence of cambic, argillic, calcic, petrocalcic horizons, aquic moisture regime, secondary carbonates, and chroma of 2 or less, were used for the prediction of soil classes in the great group and subgroup levels.
In the suborder level which presence or absence of argillic horizon and aquic condition were used for classification of soils at this level, some properties including cross-sectional curvature, slope, analytical hill shading, closed depressions, catchment area, and channel-network base level were the most effective properties in predicting soil classes.
Sensitivity analysis results for predicting soil classes by auxiliary parameters Auxiliary parameters Soil Taxonomy levels Order Suborder Great group Subgroup Family Geologic map 1.
Sensitivity analysis results for predicting soil classes using soil properties Soil properties Soil Taxonomy levels Order Suborder Great group Subgroup Family Calcic horizon - - 9 7.
For the suborder level, the soil properties of the presence and absence of argillic horizon and aquic conditions had been effective in predicting soil classes (scheme of decision tree has not been shown).
Sensitivity analysis results for predicting soil classes using combining auxiliary parameters and soil properties Properties’ name Soil Taxonomy levels Order Suborder Great group Subgroup Family Soil properties Calcic horizon - - 4 1.