Comparison of artificial neural network and decision tree methods for mapping soil units in Ardakan region

In response to the demand for soil spatial information، the acquisition of digital auxiliary data and matching it to field soil observation is increasing. With the harmonization of these data sets، through computer based methods، so-called Digital soil Maps are increasingly being found to be as reliable as traditional soil mapping practices but without the prohibitive costs. Therefore، at present research، we have attempted to develop decision tree (DTA) and artificial neural network (ANN) models for spatial prediction of soil taxonomic classes in an area covering 720 km2 located in arid region of central Iran where traditional soil survey methods are very difficult to undertake. In this area، using the conditioned Latin hypercube sampling method، location of 187 soil profiles were selected، which then described، sampled، analyzed and allocated in taxonomic classes according to soil taxonomy of America. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes، Landsat 7 ETM+ data and a geomorphologic surfaces map. Results showed that the DTA had the higher accuracy than ANN about 7% for prediction of soil classes. Determination of coefficient (R2)، overall accuracy and kappa coefficient calculated for two models were 0. 34، 0. 46، 48%، 52%، 0. 13 and 0. 25، respectively. Our results showed some auxiliary variables had more influence on predictive soil class model which included: wetness index، geomorphology map and multi-resolution index of valley bottom flatness. In general، results showed that decision tree models had higher accuracy than ANN models and also their results are more convenient for interpretation. Therefore، it is suggested using of decision tree models for spatial prediction of soil properties (category and continuous soil data) in future studies.