Journal of Geosciences and Geomatics
ISSN (Print): 2373-6690 ISSN (Online): 2373-6704 Website: Editor-in-chief: Maria TSAKIRI
Open Access
Journal Browser
Journal of Geosciences and Geomatics. 2014, 2(4), 165-171
DOI: 10.12691/jgg-2-4-4
Open AccessArticle

Support Vector Machines Based-Modeling of Land Suitability Analysis for Rainfed Agriculture

Fereydoon Sarmadian1, , Ali Keshavarzi1, Azin Rooien2, Ghavamuddin Zahedi3, Hossein Javadikia4 and Munawar Iqbal5

1Department of Soil Science, University of Tehran, Karaj, Iran

2Department of Soil Science, Shahid Chamran University, Ahvaz, Iran

3Department of Forestry, University of Tehran, Karaj, Iran

4Department of Agricultural Machinery, Razi University of Kermanshah, Kermanshah, Iran

5University of Peshawar, Peshawar, Pakistan

Pub. Date: August 19, 2014

Cite this paper:
Fereydoon Sarmadian, Ali Keshavarzi, Azin Rooien, Ghavamuddin Zahedi, Hossein Javadikia and Munawar Iqbal. Support Vector Machines Based-Modeling of Land Suitability Analysis for Rainfed Agriculture. Journal of Geosciences and Geomatics. 2014; 2(4):165-171. doi: 10.12691/jgg-2-4-4


Soil evaluation plays important role in the sustainable agriculture development. Based on the value of several soil and environment indicators, the agricultural land evaluation methodology is applied to land mapping units in order to compute the suitability index. This index characterizes these land-mapping units. However, there are different methodologies which have been reviewed for land capability and suitability evaluation. In the present study, the potential use of support vector machines (SVMs) algorithm was evaluated for land suitability analysis for rainfed wheat based on FAO land evaluation frameworks (FAO, 1976, 1983, 1985) and the proposed method by Sys et al. (1991). The study area was divided into thirteen land units (with thirty two representative soil profiles) and ten land characteristics including climatic (precipitation, temperature), topographic (relief and slope) and soil-related (texture, CaCO3, OC, coarse fragment, pH, gypsum) parameters were considered to be relevant to rainfed wheat. In this study economic factors have been excluded and moderate management has been assumed. The data points were divided by randomization technique and 80% data was selected to train the model and the remaining 20% was used to test the model. The Root Mean Square Error (RMSE) and coefficient of determination (R2) were used as evaluation criteria. The results showed that the corresponding values for RMSE and R2 between the measured and predicted land suitability indices using the SVMs model were 3.72 and 0.84 respectively. Moreover, the most important limiting factors for rainfed wheat cultivation are climatic and topographic conditions, and 84.38% of total lands are classified as S2 class (moderately suitable) while the remaining 15.62% are classified as S3 class (marginally suitable). It appears that SVMs approach could be a suitable alternative to performance of land suitability scenarios.

SVM land characteristics land suitability analysis rainfed wheat

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  Bagherzadeh A, Mansouri Daneshvar MR. 2011. Physical land suitability evaluation for specific cereal crops using GIS at Mashhad Plain, Northeast of Iran. Frontiers of Agriculture Journal, 5(4): 504-513.
[2]  Baja S, Chapman MD, Dragonvich D. 2002. A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environmental Management, 29: 647-661.
[3]  Ball A and De la Rosa D. 2006. Modelling possibilities for the assessment of soil systems. In: Uphoff N, Ball A, Fernandes E, Herren H, Husson O, Laing M, Palm Ch, Pretty J, Sanchez P, Sanginga N, Thies J (Eds.), Biological Approaches to Sustainable Soil Systems. CRC Press, BocaRaton, FL, USA.
[4]  Chong Xu, Fuchu Dai, Xiwei Xu, Yuan Hsi Lee. 2012. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145: 70-80.
[5]  Cristianini N and Scholkopf B. 2002. Support vector machines and kernel methods-the new generation of learning machines. AI Magazine, 23: 31-41.
[6]  Djurić U, Marjanović M, Šušić V. 2013. Land-Use Suitability Analysis of Belgrade City Suburbs Using Machine Learning Algorithm. Geoinformatics for City Transformation, January 21- 23, Ostrava.
[7]  FAO. 1976. A framework for land evaluation. FAO Soils Bulletin No. 32, Rome.
[8]  FAO. 1983. Guidelines: Land evaluation for rainfed agriculture. FAO Soils Bulletin. No. 52, Rome.
[9]  FAO. 1985. Guidelines: Land evaluation for irrigated agriculture. FAO Soils Bulletin, No. 55, Rome.
[10]  Guo QH, Kelly M, Graham CH. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling, 182, 75-90.
[11]  Held M, Imeson A, Montanarella L. 2003. Economic Interests and Benefits of Sustainable Use of Soils and Land Management. Joint Res. Centre Press, Ispra, Italy.
[12]  Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE. 2010. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 71: 107-127.
[13]  Kamkar B, Dorri MA, Teixeira da Silva JA. 2014. Assessment of land suitability and the possibility and performance of a canola (Brassica napus L.) - soybean (Glycine max L.) rotation in four basins of Golestan province, Iran. The Egyptian Journal of Remote Sensing and Space Sciences, In press.
[14]  Keshavarzi A, Sarmadian F. 2009. Investigation of fuzzy set theory`s efficiency in land suitability assessment for irrigated wheat in Qazvin province using Analytic hierarchy process (AHP) and multivariate regression methods. Proc. ‘Pedometrics 2009’ Conf, August 26-28, Beijing, China.
[15]  Lamorski K, Pachepsky Y, Sawiñski C, Walczak RT. 2008. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Science Society of America Journal, 72: 1243-1247.
[16]  Li H, Liang Y, Xu Q. 2009. Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95: 188-198.
[17]  Manton MG, Angelstam P, Mikusinski G. 2005. Modelling habitat suitability for deciduous forest focal species. A sensitivity analysis using different satellite land cover data. Landscape Ecology, 20: 827-839.
[18]  Marull J, Pino J, Mallarach JM, Cordobilla MJ. 2007. A land suitability index for strategic environmental assessment in metropolitan areas. Landscape and Urban Planning, 81: 200-212.
[19]  Mendas A and Delali A. 2012. Support system based on GIS and weighted sum method for drawing up of land suitability map for agriculture. Application to durum wheat cultivation in the area of Mleta (Algeria). Spanish Journal of Agricultural Research, 10(1): 34-43.
[20]  National Cartographic Center. 2010. Research Institute of NCC. Tehran, Iran (
[21]  Newhall F and Berdanier CR. 1996. Calculation of soil moisture regimes from the climatic record. Natural Resources Conservation Service, Soil Survey Investigation Report, No. 46, Pp13.
[22]  Oleszczuk P. 2007. The evaluation of sewage sludge and compost toxicity to Heterocypris incongruens in relation to inorganic and organic contaminants content. Environmental Toxicology, 22: 587-596.
[23]  Olivas GUE, Valdez LJR, Aldrete A, Gonzalez GMdJ, Vera CG. 2007. Suitable areas for establishing maguey cenizo plantations: definition through multicriteria analysis and GIS. Revista fitotecnia mexicana, 30: 411-419.
[24]  Samir MK. 1986. A statistical approach in the use of parametric system applied to the FAO framework for land evaluation. Ph.D. thesis, State University of Ghent, Belgium, 141pp.
[25]  Soil Survey Staff. 2006. Keys to Soil Taxonomy, 10th ed. United States Department of Agriculture, Washington.
[26]  Sparks DL, Page AL, Helmke PA, Leoppert RH, Soltanpour PN, Tabatabai MA, Johnston GT, Summer ME. 1996. Methods of soil analysis. Soil Science Society of America, Madison, Wisconsin.
[27]  Sys C, Van Ranst E, Debaveye IJ. 1991. Land evaluation. Part I: Principles in Land Evaluation and Crop Production Calculations. General Administration for Development Cooperation, Agricultural publication, No. 7, Brussels-Belgium, 274pp.
[28]  Sys C and Debaveye J. 1991. Land evaluation, part 1: Principles in land evaluation and crop production calculation. In: General administration for development cooperation. Brussels, Belgium.
[29]  Tang H, Debaveye J, Ruan D, Van Ranst E. 1991. Land suitability classification based on fuzzy set theory. Pedologie, 3: 277-290.
[30]  Twarakavi NKC, Simunek J, Schaap MG. 2009. Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines. Soil Science Society of America Journal, 73: 1443-1452.
[31]  Van Ranst E, Tang H, Groenemans R, Sinthurahat S. 1996. Application of Fuzzy logic to land suitability for rubber production in Peninsular Thailand. Geoderma, 70: 1-19.
[32]  Vapnik V. 1995. Nature of statistical learning theory. John Wiley and Sons Inc., New York.
[33]  Wang WC, Chau KW, Cheng CT, Qiu L. 2009. Acomparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374: 294-306.
[34]  Wilson JP and Gallant JC. 2000. Terrain analysis, principle and applications. John Wiley and Sons Inc., New York.
[35]  Wosten JHM, Pachepsky YA, Rawls WJ. 2001. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251: 123-150.
[36]  Yang L, Zhu AX, Qi F, Qin C, Li B, Pei T. 2012. An integrative hierarchical stepwise sampling strategy for spatial sampling and its application in digital soil mapping. International Journal of Geographical Information Science, 27: 1-23.