Document Type : Research Paper
Authors
1 Soil Science Dep. Faculty of Agriculture, University of Tehran
2 Professor, Department of Soil Science, University of Tehran, Karaj, Iran.
Abstract
Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. The decision tree model is a widely used method for predicting soil class in digital soil mapping studies. This study aimed to provide a digital soil mapping in four levels of taxonomy using a decision tree with Boost-reinforced C5.0 algorithm using satellite data and digital Elevation Model and geological maps as environmental variables in 41,000 hectares of Abyek Area. This area was identified using randomized gridding of the geographic location of 128 soil profiles and then described, sampled, and classified. In this research, using the principal component analysis method on environmental variables, 20 environmental variables were selected as the representative of stacking factors for modeling. Multiresolution Valley Flatness Index is the most important environmental variable that was selected as input for the model. The results of the overall accuracy of the integrated model for predicting taxonomic levels of the Order, Suborder, great group, and subgroup were shown to be 89%, 85%, 58%, and 58%, respectively. The study also examined the effect of the boosting technique on the tree model, which showed that all taxonomic levels were better predicted by using the boost model than when no boosting was used and boosting resulted in an increase in overall accuracy and kappa coefficient It turned out.
Keywords
- Adu-Poku, S. (2012). Comparing classification algorithms in data mining. MSc dissertation. Central Connecticut State University.
- Alijani, Z., Sarmadian, F. and Mousavi, R. (2014). Comparing the Accuracy of Soil Map Prepared by Geopedology and Usual Method of Iran (Case Study: Kouhin). Journal of range and watershed management, 67, 93-102.
- Behrens, T., Zhu, A., Schmidt, K. and Scholten, T. (2010). Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma, 155, 175–185.
- Beucher, A., Møller, A.B., and Greve, M.H. (2017). Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Geoderma, 352, 351-359.
- Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A. and Edwards, Jr. (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 240, 68-83.
- Daigle, J.J., Hudnall, W.H., Gabriel, W.J., Mersiovsky, E. and Nielson, R.D. (2005). The National Soil Information System (NASIS): Designing soil interpretation classes for military land-use predictions. Journal of terra mechanics, 42(3), 305-320.
- Grunwald, S. (2009). Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152(3), 195-207.
- Harris, R.J. (2001). A Primer of Multivariate Statistics. Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey.
- Hengl, T., Rossiter, D.G. and Stein, A. (2003). Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Soil Research, 41(8), 1403-1422.
- Heung, B., Bulmer, C.E. and Schmidt, M.G. (2014). Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma, 214-215, 141–154.
- Jafari, A., Finke, P.A., Van deWauw, J., Ayoubi, S. and Khademi, H. (2012). Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. Eur. J. Soil Sci, 63, 284-298.
- Jensen, J.R. (2005). Introductory Digital Image Processing a Remote Sensing Perspective. Prentice Hall Series in Geographic Information Science. Pearson Prentice Hall, Upper Saddle River, New Jersey, 526 pp.
- Kuhn, M., Weston, S., Coulter, N., Culp, M. and Quinlan, J.R. (2015). Package ‘C50’. https://cran.rproject.org/web/packages/C50/C50.pdf (accessed 20-07-16).
- Massawe, B.H., Subburayalu, S.K., Kaaya, A.K., Winowiecki, L. and Slater, B.K. (2016). Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning. Geoderma, 311, 143-148.
- McBratney, A.B., Odeh, I.O., Bishop, T.F., Dunbar, M.S. and Shatar, T.M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97(3), 293-327.
- A.B., Mendonc. M.L.¸ Santos. A. and Minasny. B. (2003). On digital soil mapping. Geoderma, 117, 3–52.
- Minasny, B. and McBratney, A.B. (2016). Digital soil mapping: a brief history and some lessons. Geoderma, 264, 301–311.
- Mirakzehi, K., Pahlavan-Rad, M.R., Shahriari, A. and Bameri, A. (2018). Digital soil mapping of deltaic soils: A case of study from Hirmand (Helmand) river delta. Geoderma, 313, 233-240.
- Møller, A.B., Iversen, B.V., Beucher, A. and Greve, M.H. (2017). Prediction of soil drainage classes in Denmark by means of decision tree classification. Geoderma, 352, 314-322.
- Neyestani, M., Sarmadian, F., Jafari, A., Keshavarzi, A. and Sharififar, A. (2021). Digital mapping of soil classes using spatial extrapolation with imbalanced data. Geoderma Regional, 26, 415-422.
- Pires, J.C.M., Martins, F.G., Sousa, S.I.V., Alvim-Ferraz, M.C.M. and Pereira, M.C. (2008). Selection and validation of parameters in multiple linear and principal component regressions. Environmental Modelling & Software, 23(1), 50-55.
- Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
- Quinlan, J.R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.), Expert systems in the micro electronic age. Edinburgh University Press.
- Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth A Course in Methods of Data Analysis. Duxbury Thomson Learning, Pacific Grove, CA, 742 pp.
- O, D., Gustavo, M.V., Maurício, R.C. and Nelson, F.F. (2105). Digital soil mapping for soil class prediction in a dry forest of Minas Gerais, Brazil. Xv congresso brasiliero de ciencia do solo, 1-4.
- Rouse Jr, J., Haas, R.H., Schell, J.A. and Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
- Schmidt, K., Behrens, T. and Scholten, T. (2008). Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma, 146(1), 138-146.
- Scull, P., Franklin, J. and Chadwick, O.A. (2005). The application of classification tree analysis to soil type prediction in a desert landscape. Ecological Modelling, 181(1), 1-15.
- Shirali, R. (2016). Classification Trees and Rule-Based Modeling Using the C5. 0 Algorithm for Self-Image Across Sex and Race in St. Louis.
- Soil Survey Staff. (2014). Keys to Soil Taxonomy (12th. ed). USDA-Natural Resources Conservation Service. National Soil Survey Center, Lincoln, NE.
- Subburayalu, S.K., Jenhani, I. and Slater, B.K. (2014). Disaggregation of component soil series on an Ohio county soil survey map using possibilistic decision trees. Geoderma, 213, 334–345.
- Taghizadeh-Mehrjardi, R., Minasny, B., McBratney, A.B., Triantafilis, J., Sarmadian, F. and Toomanian, N. (2012). Digital soil mapping of soil classes using decision trees in central Iran. In Proceedings of the 5th Global Workshop on Digital Soil Mapping, pp. 197-202.
- Taghizadeh-Mehrjardi, R., Nabiollahi, K., Minasny, B. and Triantafilis, J. (2015). Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma, 253-254, 67–77.
- Taghizadeh-Mehrjardi, R., Sarmadian, F., Minasny, B., Triantafilis, J. and Omid, M. (2014). Digital mapping of soil classes using decision tree and auxiliary data in the Ardakan region, Iran. Arid Land Research and Management, 28(2), 147-168.
- Travis, Nauman. (2009). Digital soil-landscape classification for soil survey using Aster satellite and digital elevation data in organ pipe cactus national monument. Msc dissertation in University of Arizona. 1-170.
- Wu, W., Li, A.D., He, X.H., Ma, R., Liu, H.B. and Lv, J.K. (2018). A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Computers and Electronics in Agriculture, 144, 86-93.
- Zandi, B.M. and Shekaari, P. (2019). Soil Distribution Pattern Analysis in a Low Relief Area Using Decision Trees Algorithm. Iranian Journal of Soil and Water Research, 50(2), 463-480.
- Zeraatpisheh, M., Ayoubi, S., Jafari, A. and Finke, P. (2017). Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran. Journal of Geomorphology, 285, 186-204.
- Zhu, A.X., Hudson, B., Burt, J., Lubich, K. and Simonson, D. (2001). Soil mapping using GIS, expert knowledge, and fuzzy logic. Journal of Soil Science Society of America, 65(5), 1463-1472.
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