تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,093,579 |
تعداد دریافت فایل اصل مقاله | 97,198,167 |
Application of soil properties, auxiliary parameters, and their combination for prediction of soil classes using decision tree model | ||
Desert | ||
مقاله 15، دوره 24، شماره 1، شهریور 2019، صفحه 153-169 اصل مقاله (795.32 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jdesert.2019.72449 | ||
نویسندگان | ||
M. Shahini Shamsabadi1؛ I. Esfandiarpour-Borujeni* 1؛ H. Shirani1؛ M.H. Salehi2 | ||
1Soil Science Department, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran | ||
2Soil Science Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran | ||
چکیده | ||
Soil classification systems are very useful for a simple and fast summarization of soil properties. These systems indicate the method for data summarization and facilitate connections among researchers, engineers, and other users. One of the practical systems for soil classification is Soil Taxonomy (ST). As determining soil classes for an entire area is expensive, time-consuming, and almost impossible, this research has tried to predict the soil classes in each level of the ST system (up to family level) by using the data of 120 excavated pedons and some auxiliary parameters (such as derivatives of digital elevation model, i.e., DEM) in Shahrekord plain, central Iran. For this reason, the decision tree model was encoded and implemented in the MATLAB software for three conditions: use of soil properties, auxiliary parameters, and its combination. According to the results, soil class prediction error by using soil properties, auxiliary parameters, and its combination was estimated to be 0, 3.33 and 0% for order and suborder levels; 0.83, 15 and 0.83% for great group level; 3.33, 22.5 and 3.33% for subgroup level and 30, 52.5 and 30% for family level, respectively. In addition, the use of kriging maps of soil properties (instead of 120 observational points) decreased the prediction error of the modeling in all levels of the ST system. It seems that the effect of auxiliary parameters (in comparison to soil properties) is not very significant for predicting soil classes in low-relief areas. | ||
کلیدواژهها | ||
Soil classification؛ Kriging maps؛ digital soil mapping؛ Sensitivity analysis | ||
مراجع | ||
Adhikari, K., A.E. Hartemink, 2016. Linking soils to ecosystem services - A global review. Geoderma, 262; 101–111. Bagheri Bodaghabadi, M., M.H. Salehi, J.A. Martínez- Casasnovas, J. Mohammadi, N. Toomanian, I. Esfandiarpoor Borujeni, 2011. Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications. Catena, 86; 66–74. Bagheri Bodaghabadi, M., J.A. Martínez-Casasnovas, M.H. Salehi, J. Mohammadi, I. Esfandiarpoor Borujeni, N. Toomanian, A. Gandomkar, 2015. Digital Soil Mapping using Artificial Neuronal Networks (ANN) and Terrain-Modelling Attributes. Pedosphere, 25; 580-591. Bockheim, J.G., A.E. Hartemink, 2013. Distribution and classification of soils with clay-enriched horizons in the USA. Geoderma, 209–210; 153–160. Brungard, C.W., J.L. Boettinger, M.C. Duniway, S.A. Wills, T.C. Edwards, 2015. Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239-240; 68–83. Das, M.D., 2009. Principles of Geotechnical Engineering (7th ed.), Cengage Learning, Stamford, CT. Elliott, P.E., P.J. Drohan, 2009. Clay accumulation and argillic-horizon development as influenced by aeolian deposition vs. local parent material on quartzite and limestone-derived alluvial fans. Geoderma, 151; 98– 108. Esfandiarpoor Borujeni I., J. Mohammadi, M.H. Salehi, N. Toomanian, R.M. Poch, 2010. Assessing geopedological soil mapping approach by statistical and geostatistical methods: A case study in the Borujen region, Central Iran. Catena, 82; 1–14. Geological survey and mineral exploration of Iran. 2017. http://www.gsi.ir. Goodman, J. M., P. R. Owens, 2012. Predicting soil organic carbon using mixed conceptual and geostatistical models. In: B. Minasny, B. P.Malone, A. B. McBratney (eds), Digital soil assessments and beyond (pp. 155–159). London: CRC Press. Gunal, H., M.D. Ransom, 2006. Clay illuviation and calcium carbonate accumulation along a precipitation gradient in Kansas. Catena, 68; 59–69. Heung, B., H.C. Ho, J. Zhang, A. Knudby, C.E. Bulmer, M.G. Schmidt, 2016. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma, 265; 62– 77. Holmes, K.W., E.A. Griffin, N.P. Odgers, 2015. Large- area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia. Soil Research, 53; 865–880. Jafari, A., P.A. Finke, J. VandeWauw, S. Ayoubi, H. Khademi, 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. European Journal of Soil Science, 63; 284–298. Kalavathi, K., P.V. Nimitha Safar, 2015. Performance Comparison between Naive Bayes, Decision Tree and k-Nearest Neighbor. International Journal of Emerging Research in Management and Technology, 4; 152-161. Khormali, F., S. Ghergherechi, M. Kehl, S. Ayoubi, 2012. Soil formation in loess-derived soils along a subhumid to humid climate gradient, Northeastern Iran. Geoderma, 179–180; 113–122. Lagacherie, P., S. Holmes, 1997. Addressing geographical data errors in a classification tree for soil unit prediction. International Journal of Geographical Information Science, 11; 183–198. Machado, I.R., E. Giasson, A.R. Campos, J. Janderson, F. Costa, E.B. Silva, B.R. Bonfatti, 2018. Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil. Rev Bras Cienc Solo; 42: e0170193. Massawe, B.H.J., S.K. Subburayalu, A.K. Kaaya, L. Winowiecki, B.K. Slater, 2018. Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning. Geoderma, 311; 143–148. Maynard, J.J., M.G. Johnson, 2014. Scale-dependency of LiDAR derived terrain attributes in quantitative soil- landscape modeling: Effects of grid resolution vs. neighborhood extent. Geoderma, 230–231; 29–40. McBratney, A. B., M. L. Mendonc, B. Minasny, 2003. On digital soil mapping. Geoderma, 117; 3–52. Mirakzehi, K., M. Pahlavan-Rad, A. Shahriari, 2018. Digital soil mapping of deltaic soils: A case of study from Hirmand (Helmand) river delta. Geoderma, 313; 233–240. Mohammadi, M., 1986. Semi-detailed soil studies report Chaharmahal-Va-Bakhtiari province (Shahrekord and Borujen area). Tehran, Iran. Iranian Soil and Water Research Institute. Moore, ID., R.B. Grayson, A.R. Ladson, 1991. Digital terrain modelling: a review of hydrological. geomorphological and biological applications. Hydrol Process, 5; 3-30. Mosleh, Z., M.H. Salehi, A. Jafari, I. Esfandiarpoor Borujeni, A. Mehnatkesh, 2017. Identifying sources of soil classes variations with digital soil mapping approaches in the Shahrekord plain, Iran. Environ Earth Sci, 76; 748p. Odgers, N.P., W. Sun, A.B. McBratney, B. Minasny, D. Clifford, 2014. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma, 214; 91–100. Olaya, V. F., 2004. A gentle introduction to SAGA GIS. User Manual. Germany, DC; Gottingen. Rossiter, D. G., 2000. Methodology for soil resource inventories. Lecture notes. 2nd revised version. Enschede, The Netherlands: Soil Science Division, International Institute for Aerospace Survey and Earth Science (ITC). Rouse, J. W., R. H. Hass, J. A. Schell, D.W. Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of 3rd Earth Resource Technology Satellite (ERTS) Symposium, 1; 48-62. Saunders, A. M., J. L. Boettinger, 2007. Incorporating classification trees into a pedogenic understanding raster classification methodology, Green River Basin, Wyoming, USA. In: P. Lagacherie McBratney A. B., Voltz M. (ed.), Digital Soil Mapping: An introductory perspective. Developments in Soil Science. Elsevier, Amsterdam, 31; 389-399. Scull, P., J. Franklin, O.A. Chadwick, 2005. The application of classification tree analysis to soil type prediction in a desert landscape. Ecological Modelling, 181; 1–15. Soil Survey Division Staff, 1993. Soil Survey Manual. Soil Conservation Service, U.S. Department of Agriculture Handbook 18 (Chapter 3). Soil Survey Staff, 2014. Soil taxonomy: a basic systems of soil classification for making and interpreting soil surveys (12th ed.). USDA; NRCS. Taghizadeh-Mehrjardi, R., B. Minasny, J. Triantafilis, F. Sarmadian, M. Omid, 2014. Digital mapping of soil classes using decision tree and auxiliary data in theArdakan region, Iran. Arid Land Research and Management, 42; 225-237. Taghizadeh-Mehrjardi, R., K. Nabiollahi, B. Minasny, J. Triantafilis, 2015. Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region. Iran. Geoderma, 253–254; 67–77. Thompson, J.A., J.C. Bell, C.A. Butler, 2001. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma, 100; 67–89. US Geology Survey, 2016.
Geology.com/news/2010/free-Landsat images- from- USGS-2. (http://glovis.usgs.gov). Wilson, J.P. 2012. Digital terrain modeling. Geomorphology, 137; 107–121. Wu, W., A.D. Li, X.H. He, R. Ma, H.B. Liu, J.K. Lv, 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. Zinck, J. A., 1989. Physiography and soils (Lecture notes for soil students. Soil Science Division, Soil survey courses subject matter, K6). Enschede, The Netherlands: ITC | ||
آمار تعداد مشاهده مقاله: 605 تعداد دریافت فایل اصل مقاله: 679 |