Environmental Geology

, 56:255 | Cite as

Slope stability analysis: a support vector machine approach

Original Article


Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.


Artificial Neural Network Slope stability Support Vector Machine 


  1. Bishop AW (1955) The use of slip circle in the stability of slopes. Geotechnique 5(1):7–17Google Scholar
  2. Bishop AW, Morgenstern NR (1960) Stability coefficients for earth slopes. Geotechnique 10(4):129–150Google Scholar
  3. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) 5th Annual ACM workshop on COLT. ACM, Pittsburgh, pp 144–152Google Scholar
  4. Chen WF, Liu XL (1990) Limit analysis in soil mechanics. Amsterdam, ElsevierGoogle Scholar
  5. Chen WF, Giger MW, Fang HY (1969) On the limit analysis of stability of slopes. Soils Found 9(4):23–32Google Scholar
  6. Cortes C, Vapnik VN(1995) Support vector networks. Mach Learn 20:273–297Google Scholar
  7. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machine. University Press, London, CambridgeGoogle Scholar
  8. Dibike YB, Velickov S, Solomatine D, Abbot MB (2001) Model induction with support vector machine: introduction and application. J Comput Civil Eng 15(3):208–216CrossRefGoogle Scholar
  9. Fellenius W (1936) Calculation of stability of earth dams. In: Transactions 2nd Congress on large dams, Washington 4:445Google Scholar
  10. Fletcher R (1987) Practical methods of optimization. Wiley, Chichester, NewyorkGoogle Scholar
  11. Gualtieri JA, Chettri SR, Cromp RF, Johnson LF(1999) Support vector machine classifiers as applied to AVIRIS data. In: The summaries of the 8th JPL airbrone earth science workshopGoogle Scholar
  12. Hoek E, Bray JW (1981) Rock slope engineering, 3rd edn. Institution of Mining and Metallurgy, LondonGoogle Scholar
  13. Hudson JA (1992) Rock engineering—theory and practice. Ellis Horwood, West SussexGoogle Scholar
  14. Karal K (1977a) Application of energy method. J Geotech Eng Div ASCE 103(5):381–399Google Scholar
  15. Karal K (1977b) Energy method for soil stability analyses. J Geotech Eng 103(5):431–447Google Scholar
  16. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. The MIT Press, CambridgeGoogle Scholar
  17. Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205CrossRefGoogle Scholar
  18. Lin PS, Lin MH, Lee TM (1988) An investigation on the failure of a building constructed on hillslope. In: Bonnard (ed) Landslides. Balkema, Rotterdam 1:445–449Google Scholar
  19. Madzie E (1988) Stability of unstable final slope in deep open iron mine. In: Bonnard (ed) Landslides. Balkema, Rotterdam 1:455–458Google Scholar
  20. MathWork Inc (1999) Matlab user’s manual, Version 5.3. The MathWorks, Inc, NatickGoogle Scholar
  21. Michalowski RL (1994) Limit analysis of slopes subjected to pore pressure. In: Srirwardane, Zaman (eds) Proceedings of the conference on comp. methods and advances in geomech. Balkema, RotterdamGoogle Scholar
  22. Michalowski RL (1995) Slope stability analysis: a kinematical approach. Geotechnique 45(2):283–293CrossRefGoogle Scholar
  23. Michalowski RL (2002) Stability charts for uniform slopes. J Geotech Geoenviron Eng ASCE 128(4):351–355CrossRefGoogle Scholar
  24. Morgenstern NR, Price VE (1965) The analysis of the stability of general slip surfaces. Geotechnique 15(1):79–93Google Scholar
  25. Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines. In: Proc. IEEE workshop on neural networks for signal processing, vol 7. Institute of Electrical and Electronics Engineers, New York, pp 511–519Google Scholar
  26. Muller KR, Smola A, Ratsch G, Scholkopf B, Kohlmorgen J, Vapnik VN (1997) Predicting time series with support vector machines. In: Proc. int. conf. on artificial neural networks. Springer, Berlin, pp 999Google Scholar
  27. Osuna E, Freund R, Girosi F (1997) An improved training algorithm for support vector machines. In: Proc. IEEE workshop on neural networks for signal processing, vol 7. Institute of Electrical and Electronics Engineers, New York, pp 276–285Google Scholar
  28. Park D, Rilett LR (1999) Forecasting freeway link ravel times with a multi-layer feed forward neural network. Comput Aided Civil Infrastruct Eng 4:358–367Google Scholar
  29. Sakellatiou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Int J Geotech Geol Eng 23:419–445CrossRefGoogle Scholar
  30. Scholkopf B (1997) Support vector learning. R. Oldenbourg, MunichGoogle Scholar
  31. Sincero AP (2003) Predicting mixing power using artificial neural network. EWRI World Water and EnvironmentalGoogle Scholar
  32. Smola AJ (1996) Regression estimation with support vector learning machines. Master’s Thesis: Technische Universitat Munchen, Munchen, GermanyGoogle Scholar
  33. Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222CrossRefGoogle Scholar
  34. Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkGoogle Scholar
  35. Vapnik VN, Golowich S, Smola A (1997) Support method for function approximation regression estimation and signal processing. In: Mozer M, Petsch T (eds) advance in neural information processing system, vol 9. The MIT press, CambridgeGoogle Scholar
  36. Vapnik VN (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of ScienceBangaloreIndia

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