Document Type : Research Paper

Authors

• R. Taghizadeh-Mehrjardi ¹
• F. Sarmadian ²
• M. Omid ³
• N. Toomanian ⁴
• M.J. Rousta ⁵
• M.H. Rahimian ⁶

¹ Assistant Professor, Faculty of Agriculture and Natural Resources, University of Ardakan, Yazd, Iran

² Professor, Department of Soil Science, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

³ Professor, Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

⁴ Assistant Professor, Agricultural Research Center, Isfahan, Iran

⁵ Assistant Professor, National Soil Salinity Center, Yazd, Yazd, Iran

⁶ Lecturer, National Soil Salinity Center, Yazd, Yazd, Iran

Abstract

In recent years, there has been a great development in the digital soil mapping which has led to production of maps for countries and the continents. Although many studies have been conducted all over the world, few Iranian soil scientists have shown interests in digital mapping. Therefore, in the present research, different data mining techniques (i.e. regression logistic, artificial neural network, genetic algorithm, decision tree and discriminant analysis) were applied to spatial prediction of great group soils in the area covering of 72000 ha in Ardakan. In this area, by using the conditioned Latin hypercube sampling method, location of 187 soil profiles was selected, which was 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 decision tree model had the highest accuracy while it could increase the accuracy of prediction up to 44% in comparison with discriminant analysis technique. Results also indicated using the taxonomic distances led to improving the overall accuracy of decision tree up to 3%. Results confirmed capability of decision tree, artificial neural networks, genetic algorithm, logistic regression, and discriminant analysis with 70%, 65%, 65%, 55%, and 47% accuracy, respectively. Moreover, results showed that decision tree model could predict soil classes in sub-great group with the overall accuracy of 84.2%.

Keywords

• Decision tree
• Discriminant Analysis
• Logistic Regression
• Artificial Neural Networks
• Genetic Algorithm

Main Subjects

• Soil Genesis and Classification
• Remote Sensing and GIS
• Precision Agriculture