پیش‎ بینی مکانی گروه بزرگ‎های خاک با استفاده از مدل‎های رگرسیونی و درخت تصمیم در منطقه جنوب شرق ایران

نوع مقاله: مقالات تحلیلی-تفسیری

نویسندگان

1 استادیار، دانشگاه جیرفت

2 استاد، دانشگاه صنعتی اصفهان

3 استادیار، دانشگاه باهنر کرمان

چکیده

نقشه توزیع مکانی کلاس‎های خاک برای استفاده مناسب از خاک و تصمیم‎گیری‏های مدیریتی مهم است. نقشه‏برداری رقومی خاک می‏‎تواند توزیع مکانی از کلاس‎های خاک را به صورت کمّی پیش‎بینی ‎کند. ماشین یادگیری اصطلاح کلی برای مجموعه گسترده‎ای از مدل‌ها برای کشف الگوهای موجود در داده‎ها و پیش‌بینی متغیرهای مورد مطالعه است. این مطالعه با هدف مقایسه سه مدل رگرسیون لجستیک چندجمله‎ای، رگرسیون درختی توسعه‎یافته و درخت تصمیم و کارایی آن‏ها در پیش‎بینی گروه بزرگ‎های خاک در منطقه بم استان کرمان طراحی گردید. یک طرح نمونه‎برداری طبقه‎بندی شده تصادفی در منطقه‎ای به مساحت صد هزار هکتار تعریف شد و در نهایت، ۱۲6 خاکرخ حفر و بر اساس سیستم طبقه‌بندی آمریکایی تشریح و طبقه‏بندی گردید. نتایج حاصل از مدلسازی نشان داد که نقشه سطوح ژئومرفولوژی، یک ابزار مهم در روش‌های نقشه‎برداری رقومی خاک است که به افزایش دقت پیش‌بینی کمک می‌کند. پس از سطوح ژئومرفیک، اجزای سرزمین و شاخص‌های سنجش از دور به‏عنوان پارامترهای کمکی مؤثر شناخته شدند. نتایج مقایسه دقت ارزیابی مدل‏ها نشان داد که بهترین پیش‎بینی مربوط به مدل درخت تصمیم است. این نتایج نشان می‎دهد که ساختار درختی ایجادشده بین متغیر هدف و متغیرهای انتخاب‎شده در مدل باعث افزایش دقت این مدل‏ نسبت به مدل‏های رگرسیونی شده است. نتایج کلی نشان داد که نقشه‌برداری رقومی خاک، می‎تواند به عنوان یک روش ارزیابی منابع خاک استفاده شود. علاوه بر این، قابلیت اطمینان نقشه‎های برآوردشده می‎تواند شروع یک بحث جدید بین متخصصان منابع زمین و خاکشناسان باشد. این اطلاعات همچنین می‎تواند برای تکمیل مجموعه داده‎های موجود در کشور نیز استفاده شوند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatial prediction of soil great groups by regression models and decision tree in region, southeastern Iran

نویسنده [English]

  • Farideh Abbaszadeh Afshar 1
چکیده [English]

Introduction Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. DSM is the computer-assisted production of digital maps of soil type and soil properties. It typically implies use of mathematical and statistical models that combine information from soil observations with information contained in correlated variables and remote sensing images. Machine learning is a general term for a broad set of models used to discover patterns in data and to make predictions. Although machine learning is most often applied to large databases, it is an attractive tool for learning about and making spatial predictions of soil classes because knowledge about relationships between soil classes and environmental covariates is often poorly understood. Our objective was to compare multiple machine learning models (multinomial regression logistic, boosted regression trees and decision tree) for predicting soil great groups at Bam distinct in Kerman province.
Materials and Methods The study area, Bam district was located between 58°4΄17˝ to 58°28΄8˝ E longitudes and 28°52΄51˝ to 29°9΄29˝ N latitudes (Fig. 1), at Kerman province, (Southeastern Iran). The area is surrounded by mountains (dominantly limestone and volcanic) from northwest toward southeast with major landforms included young alluvial fans and pediment, clay flat and hills. The mean annual precipitation, temperature and potential evapotranspiration are respectively 64 mm, 23.8◦C and 3000 mm with Aridic and Hyper thermic soil moisture and temperate regimes Stratified sampling scheme were defined in 100000 hectares, and 126 soil profiles were excavated and described by Key of soil taxonomy. Our objective was to perform and compare multiple machine learning models for predicting soil taxonomic classes (great group level). The models were used in this study including, multinomial logistic regression (MLR), boosted regression trees (BRT) and decision tree (DT). We used 80/20 training/testing split (80% of the pedon observations were used for model training and 20% for model testing). Kappa index (KI), overall accuracy (OC), Brier scores (BS), User accuracy (UA) and producer accuracy (PA) were used to compare model accuracy.
Results and Discussion The profile description revealed the presence of two soil orders: Entisols and Aridisols that, subdivided in six suborders and eight great groups: Haplosalids, Haplocambids, Haplocalcids, Haplogypsids, Calcigypsids, Calciargids, Petrocalcids and Torriorthents. This testifies to the wide pedodiversity of the study area, considering that is characterized by the presence of eight soils great groups. Results showed that the geomorphology map contributed importantly to the prediction accuracy. This can be explained by the fact that the geomorphological surfaces have formed recently, or during a geological period with soil formation under conditions close to those of current processes in the arid regions. Terrain attributes and finally remote sensing indices after geomorphic surface were imported as predictors in the prediction. The best prediction result was obtained when characteristics derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. The spatial distribution of soils in the study area followed the distribution pattern of most geomorphological and terrain attributes. The results of model comparing indicated that decision tree was consistently the most accurate. The results of prediction accuracy of soil groups showed that the highest accuracy related Haplosalids, Calcigypsids and Petrocalcids soil great groups. The lowest of predictive quality was observed for Haplocalcids in three approaches. As a reliable and flexible approach, decision tree could be used successfully to prepare continuous digital soil maps.
Conclusion The application of decision trees for prediction of soil types could be a promising alternative. In digital soil mapping, the best prediction result was obtained when parameters derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. Altogether, an extended digital terrain analysis approach and clear description of geomorphological, geological and pedological processes could be a promising key technology in future soil mapping.

کلیدواژه‌ها [English]

  • Digital soil mapping
  • soil great group
  • Decision tree
  • Regression Models
  1. Aksoy, E., Ozsoy, G., and Sabri Dirim, M. 2009. Soil mapping approach in GIS using Landsat satellite imagery and DEM data. African Journal Agriculture Research, 4(11): 1295-1302.
  2. Banaie, M.H. 1998. Iranian soil moisture and temperature regime map. Agricultural Research, Education and Extension Organization, Soil and Water Research Institute of Iran.
  3. Breiman L., Friedman, H.J., Olshen, A.R., and Stone, J.C. 1984. Classification and regression trees. Wadsworth Publishing Company. California, U.S.A.
  4. Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A., and Edwards Jr, T.C. 2015. Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239–240: 68–83.
  5. Elith, J., Leathwick, J.R., and Hastie, T. 2008. A working guide to boosted regression trees. Journal Animal Ecology, 77: 802–813.
  6. Esfandiarpoor Borujeni, I., Mohammadi, J., Salehi, M.H., Toomanian, N. and Poch, R. 2010. Assessing geopedological soil mapping approach by statistical and geostatistical methods: A case study in the Borujen region, Central Iran. Catena, 82: 1-14.
  7. Grunwald, S. 2005. Environmental Soil-Landscape Modeling, Geomorphic Information Technologies and Pedometrics. Taylor and Francis.
  8. Hengl, T., Heuvelink, G., and Stein, A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1–2): 75–93.
  9. Hengl, T., Toomanian, N., Reuter, H.I., and Malakouti, M.J. 2007. Methods to interpolate soil categorical variables from profile observations: lessons from Iran. Geoderma, 140: 417–427.
  10. 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. Europian Journal Soil Science, 63: 284–298.
  11. Jenny, H. 1941. Factors of soil formation. A System of Quantitative Pedology. McGraw-Hill, New York.
  12. Kempen, B., Brus, D.J., Heuvelink, G.B.M., and Stoorvoge, J.J. 2009. Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach. Geoderma, 151: 311–326.
  13. Kim, J., Grunwald, S., Rivero, R.G., and Robbins, R. 2012. Multi-scale modeling of soil series using remote sensing in a wetland ecosystem. Soil Science Society American Journal, 76: 2327–2341.
  14. Lacoste, M., Lemercier, B., and Walter, C. 2011. Regional mapping of soil parent material by machine learning based on point data. Geomorphology, 133: 90–99.
  15. McBratney, A.B., Santos, M.L.M., and Minasny, B. 2003. On digital soil mapping. Geoderma, 117(1-2): 3-52.
  16. Meteorological Organization of Iran. 2011. Meteorological statistics of Bam city, Kerman province. http://www.irimo.ir/.
  17. Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, C.B. and Bogaert, P. 2014. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, 232–234: 97–106.
  18. Schoeneberger, P. J., Wysocki, D.A., Benham, E.C., and Broderson, W.D. 2003. Field book for describing and sampling soils, Version 2.0. Natural Resources Conservation Service. National Soil Survey Center, Lincoln.
  19. 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: 1–15.
  20. Smith, C. A. S., Daneshfar, B., and Frank, G. 2012. Use of weights of evidence statistics to define inference rules to disaggregate soil survey maps. In Minasny, B., Malone, B.P., and McBratney, A. (Eds.), Digital Soil Assessments and Beyond: Proceedings of the 5th Global Workshop on Digital Soil Mapping. CRC Press, Sydney. pp: 215–220.
  21. Soil Survey Staff. 2010. Keys to Soil Taxonomy, 11th, NRCS, USDA.
  22. Sun, X.L., Yu-Guo, Z., Gan-Lin, Z., Sheng-Chun, W., Yu-Bon M. and Ming-Hung, W. 2011. Application of a digital soil mapping method in producing soil orders on mountain areas of hong kong based on legacy soil data. pedosphere, 21: 339–350.
  23. 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 Minasny, B., and McBratney M. (Eds.), Digital Soil Assessments. Taylor and Francis Group, London.
  24. 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.
  25. Taghizadeh-Mehrjardi, R., Sarmadian, F., Omid, M., Savabeghi, Gh., Toomanian, N., Rosta, M.J., and Rahimian, M.H. 2013. comparison of artificial neural network and decision tree methods for mapping soil units in ardakan region. Iranain Journal of Soil and Water Research, 44(2): 173-182.
  26. Tavares Wahren, F., Julich, S., Nunes, J.P., Gonzalez-Pelayo, O., Hawtree, D., Feger, K.H., and Jacob Keizer, J. 2016. Combining digital soil mapping and hydrological modeling in a data scarce watershed in north-central Portugal. Geoderma, 264: 350–362.
  27. The Ministry of Economy, Trade and Industry of Japan (METI) and the National Aeronautics and Space Administration (NASA). 2012. Aster Global Digital Elevation Model (Aster GDEM). NASA Official. URL http://www.gdem.aster.ersdac.or.jp.
  28. Triantifilis, J., Earl, N.Y., and Gibbs, I.D. 2012. Digital soil-classmapping across the Edgeroi district using numerical clustering and gamma-ray spectrometry data. In Minasny, B., Malone, B.P., and McBratney, A. (Eds.), Digital Soil Assessments and Beyond: Proceedings of the 5th Global Workshop on Digital Soil Mapping. CRC Press, Sydney. pp: 187–191.
  29. Zhu, A., Hudson, B., Burt, J., Lubich, K., and Simonson, D. 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society American Journal, 65: 1463-1472.