Advertisement

Fuzzy Knowledge Based GIS for Zonation of Landslide Susceptibility

  • J. K. Ghosh
  • Devanjan Bhattacharya
  • Swej Kumar Sharma
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Natural phenomena do not evolve linearly. The rapidly changing dynamics of eco-processes require modeling through non-linear concepts such as non-discreet fuzzy theory. The classification of data has been done using maximum likelihood classifier and Gaussian distribution. To better capture an event such as landslide it is imperative that the inherent continuity of the causative factors of the phenomenon are well represented. And that the uncertainties involved in assigning the probabilities of occurrences are taken care of. Decision making depends upon multiple parameters and their collective evaluation. Hence multi-criteria evaluation (MCE) of fuzzy variables has been implemented. A fuzzy knowledge-based geographical information system to categorize a given region into five levels of landslide susceptibility very high, high, moderate, low, and very low, has been presented in this chapter. The approach has used membership function for causative factors of landslide. This makes the system take into account uncertainty of the contributory factors of landslide. Due weightage has been assigned to the different spatial variations of these causative factors stored in the knowledge base. The input to the system consists of satellite data and topographic maps, as layers of information for the causative factors. The system carries out decision making by using multi-criteria evaluation of each input layer for adjusting its suitability, defined by the membership values and weights towards landslide susceptibility. The test conducted shows that the highly susceptible region extends along the zone of high rock mass thrust and slope failure and occupies 20-25% of the total study area. The very high threat occupies around 9-12% of the study area also falling on the higher slopes. On comparison of the susceptibility map with ground data, it has been found that three existing slides appear in the very high and high threat susceptible zones delineated by the system.

Keywords

Geographic Information System Landslide Susceptibility Weighted Linear Combination Landslide Susceptibility Zonation Relative Relief 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ghosh, J.K., Suri, S: A knowledge based system for assessment of landslide hazard. In Proceedings of the Indian Geotechnical Conference, vol. 1, pp. 393–396. IGS, Ahmedabad (2005)Google Scholar
  2. 2.
    Nagarajan, R., Mukherjee, A., Roy, A., Khire, M.: Temporal remote sensing data and GIS application in landslide hazard zonation of part of Western Ghat, India. Int. J. Rem. Sens. 19(4), 573–585 (1998)CrossRefGoogle Scholar
  3. 3.
    Park, N.W., Chi, K.H.: A probabilistic approach to predictive spatial data fusion for geological hazard assessment. Proc. Symp. IEEE IGARSS, France, 4, 2425–2427 (2003)Google Scholar
  4. 4.
    Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72, 272–299 (2005)CrossRefGoogle Scholar
  5. 5.
    Lee, S.I., Ryu, J., Min, K., Choi, W., Won, J.: Development and application of landslide susceptibility analysis techniques using GIS. In Proceedings of the Symposium on Geoscience and Remote Sensing, pp. 319–321. IEEE IGARSS, Honolulu (2000)Google Scholar
  6. 6.
    Muthu, K., Petrou, M.: Landslide-hazard mapping using an expert system and a GIS. IEEE Trans. Geosc. Rem. Sens. 45(2) (2007)Google Scholar
  7. 7.
    van Westen, C.J.: Digital geomorphological landslide hazard mapping of Alpago, Italy. Int. J. Appl. Earth Obs. Geoinfo.(JAG), 2(1), 51–59 (2000)Google Scholar
  8. 8.
    Piantanakulchai, N.: Analytic network process model for landslide hazard zonation. Eng. Geol. 85(3–4), 281–294 (2006)Google Scholar
  9. 9.
    Parsons, S.: Current approaches to handling imperfect information in data and knowledge bases. IEEE Trans. Know. Data Eng. 8, 353–372 (1996)CrossRefGoogle Scholar
  10. 10.
    Ghosh, J.K., Bhattacharya, D.: A knowledge based landslide susceptibility zonation system. Am. Soc. Civ. Eng. J. Comp. Civ. Eng. 24(4), 325–334 (2010), available online at:  link.aip.org/link/doi/10.1061/(ASCE)CP.1943-5487.0000034
  11. 11.
    Jankowski, P.: Integrating geographical information systems and multiple criteria decision-making methods. Int. J. Geogr. Inform. Syst. 9(3), 251–273 (1995)CrossRefGoogle Scholar
  12. 12.
    Ng, K.C., Abramson, B.: Uncertainty management in expert systems. IEEE Expert Syst. 5(2), 29–48 (1990)CrossRefGoogle Scholar
  13. 13.
    I.S. 14496, [Part 2]: Indian Standard Preparation of Landslide Hazard Zonation Maps in Mountainous Terrains – Guidelines. Part 2 Macro-Zonation. Bureau of Indian Standard, New Delhi, pp. 1–19, 1998Google Scholar
  14. 14.
    Zadeh, L.A.: Fuzzy sets. Inform. Contr. 8(3), 338–353 (1965)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Gupta, P., Anbalagan, R.: Landlide hazard zonation, mapping of Tehri-Pratapnagar area, Garhwal Himalayas. J. Rock Mech. Tunn. Tech., India. 1(1), 41–58 (1995)Google Scholar
  16. 16.
    Lai, T.: Modelling spatial dynamics of landslides: integration of GIS-based cellular automata and multicriteria evaluation methods. M.Sc. Thesis, Department of Geography, Simon Fraser University (2011)Google Scholar
  17. 17.
    Wood, L.J., Dragicevic, S.: GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection. Biodiversity Conservation 16(9): 2539–2558 (2007)CrossRefGoogle Scholar
  18. 18.
    Gorsevski, P.V., Jankowski, P.: An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Comput. Geosci. 36(8), 1005–1020 (2010)CrossRefGoogle Scholar
  19. 19.
    Jiang, H., Eastman, J.R.: Application of fuzzy measures in multi-criteria evaluation in GIS. Int. J. Geogr. Inform. Sci. 14(2), 173–184 (2000). www.tandf.co.uk/journals/tf/13658816.html Google Scholar
  20. 20.
    Malczewski, J.: GIS-based multicriteria decision analysis: a survey of the literature. Int. J. Geogr. Inform. Sci. 20(7), 703–726 (2006)CrossRefGoogle Scholar
  21. 21.
    Ayalew, L., Yamagishi, H.: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2), 15–31 (2005)CrossRefGoogle Scholar
  22. 22.
    Akgun, A., Bulut, F.: GIS-based landslide susceptibility for Arsin- Yomra (Trabzon, North Turkey) region. Environ. Geol. 51(8), 1377–1387 (2007)CrossRefGoogle Scholar
  23. 23.
    Komac, M.: A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in penialpine Slovenia. Geomorphology 74(1–4), 17–28 (2006)CrossRefGoogle Scholar
  24. 24.
    Park, N.W., Chi, K.H., Kwon, B.D.: Application of fuzzy set theory for spatial prediction of landslide hazard. In Proceedings of the Symposium on Geoscience and Remote Sensing, vol. 5, pp. 2988–2990. IEEE IGARSS, Alaska (2004)Google Scholar
  25. 25.
    Ostir, K., Veljanovski, T., Podobnikar, T., Stancic, Z.: Application of satellite remote sensing in natural hazard management. Int. J. Rem. Sens. 24(20), 3983–4002 (2003)CrossRefGoogle Scholar
  26. 26.
    Yesilnacar E., Suzen, M.L.: A land-cover classification for landslide susceptibility mapping by using feature components. Int. J. Rem. Sens. 27(2), 253–275 (2006)CrossRefGoogle Scholar
  27. 27.
    Alemayehu, D.: Assessment of flood risk in Dire-Dawa town. Master’s Thesis, Addis Ababa University, pp. 12–13, link: danielalemayehu.pdf (2007)Google Scholar
  28. 28.
    van Westen, C.J., van Asch, T.W.J., Soeters, R.: Landslide hazard and risk zonation-why is it still so difficult? Bull. Eng. Geol. Env. 65, 167–184 (2006)CrossRefGoogle Scholar
  29. 29.
    Bonham-Carter, G.F.: Geographic Information Systems for Geoscientists, Modeling with GIS, p. 398. Pergamon, Oxford (1994)Google Scholar
  30. 30.
    Lee, S.: Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ. Geol. 52, 615–623 (2007)CrossRefGoogle Scholar
  31. 31.
    Pistocchi, A., Luzi, L., Napolitano, P.: The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ. Geol. 58, 251–270 (2002)Google Scholar
  32. 32.
    Pradhan, B., Lee, S.: Landslide risk analysis using artificial neural network model focussing on different training sites. Int. J. Phys. Sci. 4(1), 001–015 (2009)Google Scholar
  33. 33.
    Murgante, B., Casas, G.L. G.I.S. and fuzzy sets for the land suitability analysis. Lecture Notes in Computer Science LNCS, vol. 3044, pp. 1036–1045, ISSN: 0302–9743. Springer-Verlag, Berlin (2004)Google Scholar
  34. 34.
    Petry, F.E., Robinson, V.B., Cobb, M.A. (eds.): Fuzzy Modeling with Spatial Information for Geographic Problems, XII, 338 p. Springer, Berlin (2005)Google Scholar
  35. 35.
    Baja, S., Chapman, D.M., Dragovich, D.: A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environ. Manag. 29(5), 647–661 (2002). Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • J. K. Ghosh
    • 1
  • Devanjan Bhattacharya
    • 1
  • Swej Kumar Sharma
    • 2
  1. 1.Civil Engineering DepartmentIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Technologist, TATA SteelJamshedpurIndia

Personalised recommendations