Classification of Landslide Susceptibility in the Development of Early Warning Systems

  • Dominik Gallus
  • Andreas Abecker
  • Daniela Richter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Statistical classification techniques complemented by the use of GIS have been shown to yield good results at the task of an assessment of landslide hazard/ susceptibility. In this work, several classification methods previously applied to this task are compared with respect to their performance on data sampled from distinct alpine areas in Vorarlberg, Austria. It is shown that among different types of techniques, kernel methods, including the Support Vector Machine and the Gaussian Process model, outperform techniques traditionally employed for the task. As further result, hazard maps for the study areas are generated, which can be used as input for suitable early warning systems focussing on landslide hazard.


landslide classification early warning system GIS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Atkinson P. M., and Massari, R. (1998) Generalized linear modelling of susceptibility to landslides in the Central Apennines, Italy. Computers and Geosciences, 24.Google Scholar
  2. Bishop C. M. (2006) Pattern Recognition and Machine Learning, Springer.Google Scholar
  3. Brenning A. (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5.Google Scholar
  4. Cortes C., and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20.Google Scholar
  5. Cressie N. A. C. (1993) Statistics for spatial data,New York, John Wiley & Sons.Google Scholar
  6. Ermini L., Catani, F., and Casagli, N. (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66.Google Scholar
  7. Gorsevski P. V., Gessler, P.E., and Foltz, R.B. (2000a) Spatial prediction of landslide hazard using discriminant analysis and GIS. GIS in the Rockies 2000 Conference and Workshop.Denver, Colorado, USA.Google Scholar
  8. Gorsevski P. V., Gessler, P.E., and Foltz, R.B. (2000b) Spatial prediction of landslide hazard using logistic regression and GIS. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4).Banff, Alberta, Canada.Google Scholar
  9. Gotway C. A., and Stroup, W. W. (1997) A generalized linear model approach to spatial data analysis and prediction. Journal of Agricultural, Biological, and Environmental Statistics, 2.Google Scholar
  10. Karatzoglou A., Smola J., Hornik, K., and Zeileis, A. (2004) kernlab – An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11.Google Scholar
  11. Lee S., Ryu, J.-H., Min, K., and Won, J.-S. (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms, 28.Google Scholar
  12. Mackay D. J. C. (1998) Introduction to Gaussian processes. IN BISHOP, C. M. (Ed.) Neural Networks and Machine Learning. Springer.Google Scholar
  13. Ohlmacher G. C., and Davis, J. C. (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas, USA. Engineering Geology, 69.Google Scholar
  14. Platt J. C. (1999) Fast training of support vector machines using sequential minimal optimization. IN Schoelkopf, B., Burges, C., and Smola, A. (Ed.) Advances in Kernel Methods - Support Vector Learning.Cambridge, MIT Press.Google Scholar
  15. Platt J. C. (2000) Probabilistic outputs for Support Vector Machines and comparison to regularized likelihood methods. In Smola, A., Bartlett, P., Schoelkopf, B., and Schuurmans, D. (Ed.) Advances in Large-Margin Classifiers.Cambridge, Massachusetts, USA, MIT Press.Google Scholar
  16. Ruff M., KÜhn, M., and Czurda, K. (2005) Risikoanalyse für Massenbewegungen in den Ostalpen (Vorarlberg). IN Moser, M. (Ed.) 15. Tagung Ingenieurgeologie.Erlangen, Germany.Google Scholar
  17. Santacana N., Baeza, B., Corominas, J., de Paz, A., and Marturia, J. (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet area (Eastern Pyrenees, Spain). Natural Hazards, 30.Google Scholar
  18. Vapnik V. (1998) Statistical Learning Theory,New York, John Wiley and Sons.Google Scholar
  19. Williams C. K. I., and Barber, D. (1998) Bayesian Classification with Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20.Google Scholar
  20. Williams C. K. I., and Rasmussen, C.E. (1995) Gaussian Processes for regression. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E. (Ed.) Neural Information Processing Systems.Denver, Colorado, USA, MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dominik Gallus
    • 1
  • Andreas Abecker
    • 1
  • Daniela Richter
    • 2
  1. 1.Research Center for Information Technologies (FZI)Germany
  2. 2.Institute for Photogrammetry and Remote Sensing (IPF)University of Karlsruhe (TH)Germany

Personalised recommendations