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Classification of Landslide Susceptibility in the Development of Early Warning Systems

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Headway in Spatial Data Handling

Abstract

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.

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References

  • 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 

  • Bishop C. M. (2006) Pattern Recognition and Machine Learning, Springer.

    Google Scholar 

  • Brenning A. (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5.

    Google Scholar 

  • Cortes C., and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20.

    Google Scholar 

  • Cressie N. A. C. (1993) Statistics for spatial data,New York, John Wiley & Sons.

    Google Scholar 

  • Ermini L., Catani, F., and Casagli, N. (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • Mackay D. J. C. (1998) Introduction to Gaussian processes. IN BISHOP, C. M. (Ed.) Neural Networks and Machine Learning. Springer.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • Vapnik V. (1998) Statistical Learning Theory,New York, John Wiley and Sons.

    Google Scholar 

  • Williams C. K. I., and Barber, D. (1998) Bayesian Classification with Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20.

    Google Scholar 

  • 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 

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Gallus, D., Abecker, A., Richter, D. (2008). Classification of Landslide Susceptibility in the Development of Early Warning Systems. In: Ruas, A., Gold, C. (eds) Headway in Spatial Data Handling. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68566-1_4

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