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Landslides

, Volume 6, Issue 1, pp 17–26 | Cite as

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

  • John Mathew
  • V. K. Jha
  • G. S. Rawat
Original Article

Abstract

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.

Keywords

Landslide GIS Binary logistic regression 

Notes

Acknowledgements

The authors sincerely thank Dr. V. Jayaraman, Director, NNRMS/EOS, Department of Space, ISRO, India, for permitting them to carry out the study. JM thanks Dr. K. P. Sharma, Head, RRSSC, Dehradun, for the constant encouragement and motivation. Dr. N. S. Virdi, Former Director and Dr. G. Philip, Scientist, Wadia Institute of Himalayan Geology, Dehradun, extended their help and support for carrying out this study. Prof. M. L. Süzen, METU, Turkey and Prof. John C. Davis, Department of Petroleum Engineering, Leoben, Austria provided valuable suggestions on logistic regression model.

References

  1. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  2. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25(3):559–572CrossRefGoogle Scholar
  3. Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385CrossRefGoogle Scholar
  4. Barredo J, Benavides A, Hervas J, van Westen C (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. JAG 2(1):9–23Google Scholar
  5. Begueria S, Lorente A (2002) Landslide hazard mapping by multivariate statistics: comparison of methods and case study in the Spanish Pyrenees. Technical report. Instituto Pirenaico de Ecologia, Saragossa, p 19Google Scholar
  6. Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15(3):403–427CrossRefGoogle Scholar
  7. Carrara A (1988) Landslide hazard mapping by statistical methods: a black-box approach. In: Proceeding of workshop on natural disaster in European Mediterranean Countries, Perugia, Italy, pp 205–224Google Scholar
  8. Carrara A, Cardinalli M, Detti R, Guzzetti F, Pasquvi V, Reichenbach P (1990) Geographical information systems and multivariate models in landslide hazard evaluation. In: Proceeding of ALPS 90, Alpine Landslide Practical Seminar, Sixth International Conference and Field workshop on landslides, Milan, Italy, 31 August–12 September 1990, pp 17–28Google Scholar
  9. Carrara A, Cardinalli M, Detti R, Guzzetti F, Pasquvi V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Processes Landf 16(5):427–445CrossRefGoogle Scholar
  10. Carrara A, Cardinalli M, Guzzetti F (1992) Uncertainty in assessing landslide hazard and risk. ITC J 2:172–183Google Scholar
  11. Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Processes Landf 28:1125–1142CrossRefGoogle Scholar
  12. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modelling using GIS in Lantau Island, Hong Kong. Geomorphology 42:213–238CrossRefGoogle Scholar
  13. Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391CrossRefGoogle Scholar
  14. Davis JC, Ohlmacher GC (2002) Landslide hazard prediction using generalized logistic regression. In: Proceedings of 8th Annual Conference of the International Association for Mathematical Geology, Berlin, Germany, pp 501–506Google Scholar
  15. Donati L, Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289CrossRefGoogle Scholar
  16. Gorsevski PV, Gessler P, Foltz RB (2000a) Spatial prediction of landslide hazard using discriminant analysis and GIS. In: Proceedings of GIS in the Rockies 2000: Conference and Workshop Applications for the 21st Century, Denver, Colorado, 25 – 27 September 2000, p 10Google Scholar
  17. Gorsevski PV, Gessler PE, Foltz RB (2000b) Spatial prediction of landslide hazard using logistic regression and GIS. In: Proceedings of the 4th International Conference on Integrating GIS and Environmental Modelling: Problems, Prospects and Research Needs, Banff, Alberta, 2–8 September 2000, p 10Google Scholar
  18. GSI (2005) Geological quadrangle map 53J. Geological Survey of India, CalcuttaGoogle Scholar
  19. Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131CrossRefGoogle Scholar
  20. Gupta V, Sah MP, Virdi NS, Bartarya SK (1993) Landslide hazard zonation in the Upper Satlej Valley, District Kinnaur, Himachal Pradesh. J Himal Geol 4:81–93Google Scholar
  21. Gupta RP, Saha AK, Arora MK, Kumar A (1999) Landslide hazard zonation in a part of Bhagirathy Valley, Garhwal Himalayas, using integrated remote sensing—GIS. J Himal Geol 20(2):71–85Google Scholar
  22. Hair JF, Black B, Babin B, Anderson RE, Tatham RL (2006) Multivariate data analysis, 6th edn. Prentice Hall, London, p 928Google Scholar
  23. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36Google Scholar
  24. Johnson DE (1998) Applied multivariate methods for data analysis. Duxbury, Pacific GroveGoogle Scholar
  25. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366CrossRefGoogle Scholar
  26. Kleinbaum DG (1994) Logistic regression: a self learning text. Springer, New York, p 282Google Scholar
  27. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491CrossRefGoogle Scholar
  28. Lee S, Evangelista DG (2005) Landslide susceptibility mapping using probability and statistics models in Baguio City, Philippines. In: Proceedings of the 31st International Symposium on remote sensing of environment, 20–24 June, 2005, St. Petersburg, Russia, p 4Google Scholar
  29. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  30. Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–220CrossRefGoogle Scholar
  31. Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci 92(5):628–638Google Scholar
  32. NRSA (2001) Landslide hazard zonation mapping along the corridors of the pilgrimage routes in Uttaranchal Himalaya. Technical document, NRSA, Department of Space, IndiaGoogle Scholar
  33. Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343CrossRefGoogle Scholar
  34. Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32:81–100CrossRefGoogle Scholar
  35. Panikkar SV (1995) Landslide hazard zonation in Mussoorie Hills. Ph.D. Thesis, Department of Earth Sciences, IIT Bombay, p 136Google Scholar
  36. Panikkar SV, Subramanyan V (1996) A geomorphic evaluation of landslides around Dehradun and Mussoorie, Uttar Pradesh, India. Geomorphology 15:169–181CrossRefGoogle Scholar
  37. Philip G, Ravindran KV, Mathew J (2003) Mapping the Nidar ophiolite complex of the Indus Suture Zone, Northwestern-Trans Himalaya using IRS-1C/1D data. Int J Remote Sens 24(24):4979–4994CrossRefGoogle Scholar
  38. Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in a part of the Himalayas. Int J Remote Sens 23:357–369CrossRefGoogle Scholar
  39. Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69CrossRefGoogle Scholar
  40. Suzen ML (2002) Data driven landslide hazard assessment using geographical information systems and remote sensing. Ph.D. Thesis, Middle East Technical University, Turkey, p 196Google Scholar
  41. Süzen M, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679CrossRefGoogle Scholar
  42. Valdiya KS (1980) Geology of Kumaun lesser Himalaya. Wadiya Institute of Himalayan Geology, Dehradun, p 291Google Scholar
  43. Zweig MH, Campbell G (1993) Receiver operating characteristic plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39(4):561–577Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.RRSSC, ISRO, Department of SpaceIndian Space Research OrganizationDehradunIndia
  2. 2.IIRS, ISRO, Department of SpaceIndian Space Research OrganizationDehradunIndia
  3. 3.Department of GeologyHNB Garhwal UniversitySrinagarIndia

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