Artificial Neural Network and Decision Tree in Predictive Soil Mapping of Hoi Num Rin Sub-Watershed, Thailand

Abstract

The demand for high-resolution soil mapping is growing increasingly, in particular for the purpose of land degradation studies. The objective of this study focuses on applying the methods for digital predictive soil mapping in inaccessible, land degradation-prone areas. Artificial Neural Network (ANN) and Decision Tree (DT) were employed within the GIS environment to comply with the complexity of the soil forming factors governing the soil formation. Following the principles of the geopedologic approach to soil survey, a digital predictive soil mapping was carried out in Hoi Num Rin sub-watershed, covering an area about 20 km². Both ANN and DT were applied to properly integrate the parameterized soil forming factors. To describe soil predictors to train the ANN and to formulate the decision trees, 4 organism types, 7 relief type units, 9 lithological units, 3 time series, 4 landscape units and 8 landform units were extracted from the map and databases. The results, the 10 soil class names were extrapolated to the unsampled areas to obtain the geopedologic map. In conclusion, the geopedologic approach to soil survey, which is based on understanding of landscape-soil relationship, is helpful to obtain spatial soil information in inaccessible areas, using ANN and/or DT are useful techniques in modeling the complex interactions among the soil forming factors. The difference, however, is that ANN, once it is well learnt, is faster, thus more recommendable in terms of time and cost saving.