Advertisement

Potential Landslide Vulnerability Zonation Using Integrated Analytic Hierarchy Process and GIS Technique of Upper Rangit Catchment Area, West Sikkim, India

  • Subodh Chandra PalEmail author
  • Biswajit Das
  • Sadhan Malik
Research Article
  • 15 Downloads

Abstract

Landslides have become a serious hazard in mountainous region where exhaustive storm rainfall is common. The intensity of landslide hazard is not only depending upon storm rainfall and physical factors but also depends upon several anthropogenic activities or land use practices. The aim and objective of this paper are to highlight the landslide vulnerability with the help of analytical hierarchy process and geographic information system (GIS) tools. Weighted overlay analysis method was used for assessing landslide vulnerability of upper Rangit catchment area, West Sikkim, India, within the GIS environment. Weights and rank are assigned on the basis of influence of landslide occurrence. Factors selected for this study include rock type, geomorphology, slope, aspect, drainage density, soil type and land use and land cover. The landslide vulnerability map will provide valuable information to the local government for planning and management in future. Higher weight was assigned for greater influence and lower weight was assigned for lesser influence of landslide. Southern part and middle part of the region have very high vulnerability of landslide in comparison with the northern region, but the region having much anthropogenic activities towards the south became more vulnerable. It has been observed that 38.48% of the area is at very low vulnerability, 32.26% is at low vulnerability, 18.16% at medium vulnerability, 8.56% at high vulnerability and 2.53% at very high vulnerability.

Keywords

Landslide vulnerability mapping Analytical hierarchy process Remote sensing Geographic information system 

Notes

Acknowledgements

Authors are grateful to Vinay Kumar Dadhwal, Section Editor, Journal of the Indian Society of Remote Sensing, and anonymous reviewers for their valuable suggestions in regard to the improvement of this research article. The authors are also grateful to the Department of Geography, The University of Burdwan, for providing the infrastructural facilities and at the same time thankful to the students of special paper Geomorphology of the session of 2014–2016 for helping in the field work. We are thankful to the different government and non-government authorities for providing the useful data. We are also thankful to those people who helped in different stages of this work.

References

  1. Alexander, D. (2005). Vulnerability to landslides. In T. Glade, M. Anderson, & M. J. Crozier (Eds.), Landslide hazard and risk (pp. 175–198). Chichester: Wiley.Google Scholar
  2. Atkinson, P. M., & Massari, R. (1998). Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24(4), 373–385.  https://doi.org/10.1016/S0098-3004(97)00117-9.Google Scholar
  3. Bathrellos, G. D., Gaki-Papanastassiou, K., Skilodimou, H. D., Papanastassiou, D., & Chousianitis, K. G. (2012). Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters. Environmental Earth Sciences, 66(2), 537–548.Google Scholar
  4. Bathrellos, G. D., Kalivas, D. P., & Skilodimou, H. D. (2009). GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala, Central Greece. Estud Geol, 65(1), 49–65.  https://doi.org/10.3989/egeol.08642.036.Google Scholar
  5. Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427–445.  https://doi.org/10.1002/esp.3290160505.Google Scholar
  6. Catlos, E. J., Dubey, C. S., Harrison, T. M., & Edwards, M. A. (2004). Late Miocene movement within the Himalayan Main Central Thrust shear zone, Sikkim, north-east India. Journal of Metamorphic Geology, 22(3), 207–226.  https://doi.org/10.1111/j.1525-1314.2004.00509.x.Google Scholar
  7. Chau, K. T., Sze, Y. L., Fung, M. K., Wong, W. Y., Fong, E. L., & Chan, L. C. P. (2004). Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers & Geosciences, 30(4), 429–443.  https://doi.org/10.1016/j.cageo.2003.08.013.Google Scholar
  8. Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., et al. (2018a). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of the Total Environment, 626, 1121–1135.  https://doi.org/10.1016/j.scitotenv.2018.01.124.Google Scholar
  9. Chen, W., Shahabi, H., Shirzadi, A., Li, T., Guo, C., Hong, H., et al. (2018b). A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto International, 33(12), 1398–1420.  https://doi.org/10.1080/10106049.2018.1425738.Google Scholar
  10. Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D. T., et al. (2018c). GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA, 164, 135–149.  https://doi.org/10.1016/j.catena.2018.01.012.Google Scholar
  11. Coppock, J. T. (1995). GIS and natural hazards: An overview from a GIS perspective. In A. Carrara & F. Guzzetti (Eds.), Geographical information systems in assessing natural hazards (pp. 21–34). Netherlands: Springer.  https://doi.org/10.1007/978-94-015-8404-3_2.Google Scholar
  12. Cruden, D. M. (1991). A simple definition of a landslide. Bulletin of Engineering Geology and the Environment, 43(1), 27–29.  https://doi.org/10.1007/BF02590167.Google Scholar
  13. Dai, F. C., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: An overview. Engineering Geology, 64(1), 65–87.  https://doi.org/10.1016/S0013-7952(01)00093-X.Google Scholar
  14. Dikau, R., Cavallin, A., & Jäger, S. (1996). Databases and GIS for landslide research in Europe. Geomorphology, 15(3–4), 227–239.  https://doi.org/10.1016/0169-555X(95)00072-D.Google Scholar
  15. Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75(3), 229–250.  https://doi.org/10.1016/j.enggeo.2004.06.001.Google Scholar
  16. Gupta, M., Ghose, M. K., & Sharma, L. P. (2009). Application of remote sensing and GIS for landslides hazard and assessment of their probabilistic occurrence—a case study of NH31A between Rangpo and Singtam. J Geomatics, 3(1), 13–17.Google Scholar
  17. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1), 181–216.  https://doi.org/10.1016/s0169-555x(99)00078-1.Google Scholar
  18. Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491.  https://doi.org/10.1080/01431160412331331012.Google Scholar
  19. Lee, S. (2007). Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology, 52(4), 615–623.  https://doi.org/10.1007/s00254-006-0491-y.Google Scholar
  20. Lee, S., & Pradhan, B. (2006). Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661–672.Google Scholar
  21. Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003). Landslide susceptibility analysis using artificial neural network at Boun, Korea. Environmental Geology, 44, 820–833.Google Scholar
  22. Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855.  https://doi.org/10.1007/s00254-006-0256-7.Google Scholar
  23. Leone, F., Asté, J. P., & Leroi, E. (1996). Vulnerability assessment of elements exposed to mass-movement: Working toward a better risk perception. In K. Senneset (Ed.), Landslides-Glissements de Terrain (pp. 263–270). Rotterdam: Balkema.Google Scholar
  24. Leroi, E. (1997). Landslide risk mapping: Problems, limitations and developments. Landslide risk assessment (pp. 239–250). Rotterdam: Balkema.Google Scholar
  25. Li, T., Tian, Y., Xiao, C., & Zhao, W. (2013). Slope location-based landslide vulnerability assessment. In 21st International Conference on Geoinformatics (GEOINFORMATICS) (pp. 1–4). IEEE.Google Scholar
  26. Miles, S. B., & Ho, C. L. (1999). Rigorous landslide hazard zonation using Newmark’s method and stochastic ground motion simulation. Soil Dynamics and Earthquake Engineering, 18(4), 305–323.  https://doi.org/10.1016/S0267-7261(98)00048-7.Google Scholar
  27. Miles, S. B., & Keefer, D. K. (1999). Comparison of seismic slope-performance models: Case study of the Oakland East Quadrangle, California. Reston: US Department of the Interior, US Geological Survey.Google Scholar
  28. Miller, D. J., & Sias, J. (1998). Deciphering large landslides: Linking hydrological, groundwater and slope stability models through GIS. Hydrological Processes, 12(6), 923–941.Google Scholar
  29. Pachauri, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36(3), 325–334.  https://doi.org/10.1007/s002540050348.Google Scholar
  30. Pachauri, A. K., & Pant, M. (1992). Landslide hazard mapping based on geological attributes. Engineering Geology, 32(1–2), 81–100.  https://doi.org/10.1016/0013-7952(92)90020-Y.Google Scholar
  31. Pal, S. C., & Chowdhuri, I. (2019). GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences, 1(5), 416.  https://doi.org/10.1007/s42452-019-0422-7.Google Scholar
  32. Pardeshi, S. D., Autade, S. E., & Pardeshi, S. S. (2013). Landslide hazard assessment: Recent trends and techniques. SpringerPlus, 2(1), 523.  https://doi.org/10.1186/2193-1801-2-523.Google Scholar
  33. Pascale, S., Sdao, F., & Sole, A. (2010). A model for assessing the systemic vulnerability in landslide prone areas. Natural Hazards and Earth System Sciences, 10(7), 1575–1590.  https://doi.org/10.5194/nhess-10-1575-2010.Google Scholar
  34. Patanakanog, B. (2001). Landslide hazard potential area in 3 dimension by remote sensing and GIS technique. Thailand: Land Development Department.Google Scholar
  35. Pistocchi, A., Luzi, L., & Napolitano, P. (2002). The use of predictive modeling techniques for optimal exploitation of spatial databases: A case study in landslide hazard mapping with expert system-like methods. Environmental Geology, 41(7), 765–775.  https://doi.org/10.1007/s002540100440.Google Scholar
  36. Pradhan, B., Abokharima, M. H., Jebur, M. N., & Tehrany, M. S. (2014). Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards, 73(2), 1019–1042.  https://doi.org/10.1007/s11069-014-1128-1.Google Scholar
  37. Ramakrishnan, D., Ghose, M. K., Chandran, R. V., & Jeyaram, A. (2005). Probabilistic techniques, GIS and remote sensing in landslide hazard mitigation: A case study from Sikkim Himalayas, India. Geocarto International, 20(4), 53–58.  https://doi.org/10.1080/10106040508542364.Google Scholar
  38. Refice, A., & Capolongo, D. (2002). Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Computers & Geosciences, 28(6), 735–749.  https://doi.org/10.1016/S0098-3004(01)00104-2.Google Scholar
  39. Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.  https://doi.org/10.1016/0377-2217(90)90057-I.Google Scholar
  40. Sarkar, S., Roy, A. K., & Martha, T. R. (2013). Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas. Journal of the Geological Society of India, 82(4), 351–362.  https://doi.org/10.1007/s12594-013-0162-z.Google Scholar
  41. Shahabi, H., Khezri, S., Ahmad, B. B., & Hashim, M. (2014). Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA, 115, 55–70.  https://doi.org/10.1016/j.catena.2013.11.014.Google Scholar
  42. Sharma, L. P., Patel, N., Ghose, M. K., & Debnath, P. (2014). Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: A case study from the Sikkim Himalayas in India. Geocarto International, 29(2), 128–146.  https://doi.org/10.1080/10106049.2012.748830.Google Scholar
  43. Shu-Quiang, W., & Unwin, D. J. (1992). Modelling landslide distribution on loess soils in China: An investigation. International Journal of Geographical Information Systems, 6(5), 391–405.  https://doi.org/10.1080/02693799208901922.Google Scholar
  44. Sikkim Tourism. (2017). Accessed date 22 June 2017. www.sikkimtourism.gov.in.
  45. Uromeihy, A., & Mahdavifar, M. R. (2000). Landslide hazard zonation of the Khorshrostam area, Iran. Bulletin of Engineering Geology and the Environment, 58(3), 207–213.  https://doi.org/10.1007/s100640050076.Google Scholar
  46. Varnes, D. J. (1984). Landslide hazard zonation: A review of principles and practice, P. 63. Paris: UNESCO Press.Google Scholar
  47. Vezzoli, G., Lombardo, B., & Rolfo, F. (2017). Petrology of the Tista and Rangit river sands (Sikkim, India). Italian Journal of Geosciences, 136(1), 103–109.  https://doi.org/10.3301/IJG.2016.04.Google Scholar
  48. Wallemacq, P. (2018). Economic Losses, Poverty & Disasters: 19982017. Centre for Research on the Epidemiology of Disasters, CRED. https://www.unisdr.org/we/inform/publications/61119.
  49. Wikipedia. (2017). Accessed date 24 June 2017. https://en.wikipedia.org/wiki/Rangeet_River.
  50. Yilmaz, I. (2009a). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6), 1125–1138.  https://doi.org/10.1016/j.cageo.2008.08.007.Google Scholar
  51. Yilmaz, I. (2009b). A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bulletin of Engineering Geology and the Environment, 68(3), 297–306.  https://doi.org/10.1007/s10064-009-0185-2.Google Scholar
  52. Yilmaz, I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), 821–836.  https://doi.org/10.1007/s12665-009-0394-9.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  • Subodh Chandra Pal
    • 1
    Email author
  • Biswajit Das
    • 1
  • Sadhan Malik
    • 1
  1. 1.Department of GeographyThe University of BurdwanBurdwanIndia

Personalised recommendations