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Intelligent Analysis of Landslide Data Using Machine Learning Algorithms

  • Natan MichelettiEmail author
  • Mikhail Kanevski
  • Shibiao Bai
  • Jian Wang
  • Ting Hong
Chapter

Abstract

Landslide susceptibility maps are useful tools for natural hazards assessments. The present research concentrates on an application of machine learning algorithms for the treatment and understanding of input/feature space for landslide data to identify sliding zones and to formulate suggestions for susceptibility mapping. The whole problem can be formulated as a supervised classification learning task. Support Vector Machines (SVM), a very attractive approach developing nonlinear and robust models in high dimensional data, is adopted for the analysis. Two real data case studies based on Swiss and Chinese data are considered. The differences of complexity and causalities in patterns of different regions are unveiled. The research shows promising results for some regions, denoted by good performances of classification.

Keywords

Landslide susceptibility mapping Machine learning Support vector machines 

Notes

Acknowledgments

This research was partly supported by Sino-Swiss cooperation project EG 42-032010, Swiss National Science Foundation, project “GeoKernels: kernel-based methods for geo- and environmental sciences, Phase II: 200020-121835/1” and National Natural Science Foundation of China (Nos. 40801212).

We would like to thank A. Pedrazzini and M. Jaboyedoff for their important contribution in data gathering and the indispensable knowledge in the field of landslides they provided to the current research. We also are grateful to L. Foresti, G. Matasci and M. Volpi for all interesting discussion and valuable help.

References

  1. Brenning A (2005) Spatial prediction models for landslide hazards: review comparison and evaluation. Nat Hazard Earth Syst Sci 5:835–862CrossRefGoogle Scholar
  2. Cherkassky V, Mulier F (2007) Learning from data: concepts, theory and methods. John Wiley & Sons, Inc., Hoboken, New JerseyCrossRefGoogle Scholar
  3. Foresti L, Tuia D, Kanevski M, Pozdnoukhov A (2011) Learning wind fields with multiple kernels. Stoch Environ Res Risk Assess 25(1):55–66CrossRefGoogle Scholar
  4. Kanevski M, Pozdnoukhov A, Timonin V (2009) Machine learning for spatial environmental data: theory, applications and software. EPFL Press, LausanneCrossRefGoogle Scholar
  5. Micheletti N (2011) Landslide susceptibility mapping using adaptive support vector machines and feature selection, M.S. thesis, University of Lausanne, SwitzerlandGoogle Scholar
  6. Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  7. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machines: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Natan Micheletti
    • 1
    Email author
  • Mikhail Kanevski
    • 1
  • Shibiao Bai
    • 2
  • Jian Wang
    • 2
  • Ting Hong
    • 2
  1. 1.Institute of Geomatics and Risk AnalysisUniversity of LausanneLausanneSwitzerland
  2. 2.Key Laboratory of Virtual Geographic EnvironmentsNanjing Normal UniversityNanjingChina

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