Pedosphere 25(4): 580--591, 2015
ISSN 1002-0160/CN 32-1315/P
©2015 Soil Science Society of China
Published by Elsevier B.V. and Science Press
Digital soil mapping using artificial neural networks and terrain-related attributes
M. BAGHERI BODAGHABADI1, J. A. MARTÍNEZ-CASASNOVAS2, M. H. SALEHI3, J. MOHAMMADI3, I. ESFANDIARPOOR BORUJENI4, N. TOOMANIAN5 and A. GANDOMKAR1
1Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad 8514143131 (Iran)
2Department of Environmental and Soil Science, University of Lleida, Lleida 25191 (Spain)
3Department of Soil Science, College of Agriculture, Shahrekord University, Shahrekord 8818634141 (Iran)
4Department of Soil Science, College of Agriculture, Vali-e-Asr University, Rafsanjan 7713936417 (Iran)
5Agriculture and Natural Resource Research Center, Isfahan 81785199 (Iran)
ABSTRACT
      Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
Key Words:  digital elevation model attributes, multilayer perceptron, soil classification, soil-forming factors, soil survey
Citation: Bagheri, B., MartÍnez-casasnovas, J. A., Salehi, M. H., Mohammadi, J., Esfandiarpoor, B., Toomanian, N. and Gandomkar, A. 2015. Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere. 25(4): 580-591.
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