Advertisement

Landslide Susceptibility Zonation Mapping: A Case Study from Darjeeling District, Eastern Himalayas, India

  • Amit ChawlaEmail author
  • Srinivas Pasupuleti
  • Sowmiya Chawla
  • A. C. S. Rao
  • Kripamoy Sarkar
  • Rajesh Dwivedi
Research Article
  • 29 Downloads

Abstract

Landslides have been one of the most damaging natural hazards in the hilly region, which cause loss of life and infrastructure, and hence, landslide susceptibility zonation (LSZ) maps are inevitable for the pre-identification of vulnerable slopes and for the future planning and mitigation programmes. In this study, an integrated remote sensing and geographic information system approach is adopted for the generation of LSZ Map for the Darjeeling and Kalimpong district, West Bengal, India. Topographic maps, satellite data, other informative maps and statistics were utilized. For this study, the causative factors which cause instability of slope such as drainage, lineament, slope, rainfall, earthquake, lithology, land use, geomorphology, soil, aspect and relief were considered. For the generation of LSZ map, thematic data layers were evaluated and generated by assigning appropriate numerical values for each factor weight and their corresponding class rating in the GIS environment. Resulting LSZ map outlines the total study area into five different susceptibility classes: very high, high, moderate, low and very low. This study also demonstrates the classification and prediction of landslide-susceptible zones in coalition with GIS output by using particle swarm optimization–support vector machine approach without feature selection and ant colony optimization approach with feature selection along with support vector machine classifier. GIS-based LSZ map was validated by comparing the landslide frequencies in between the susceptible classes. The usefulness of the LSZ map was also validated by the statistical Chi-square test.

Keywords

Landslide susceptibility zonation (LSZ) Geographic information system (GIS) Remote sensing (RS) 

Notes

Acknowledgements

The author(s) thank Centre for Seismology, New Delhi, India, Survey of India, Kolkata, Geological Survey of India, Kolkata, India, National Atlas and Thematic Mapping Organisation, Kolkata, India, and National Bureau of Soil Survey and Land Use Planning, Kolkata, India, etc. for providing various data used in this study. The author(s) are grateful to Mr. Harish Sinha, CHiPS, India, and Dr. Shantanu Sarkar, CBRI, Roorkee, India, for their valuable suggestions. Support from the Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India, Public Works Department, Delhi, India, and Central Public Works Department, Delhi, India, is also acknowledged.

Compliance with Ethical Standards

Conflict of interest

The author(s) declare that they have no conflict of interest.

References

  1. Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary, review and new perspectives. Bulletin of Engineering Geology and the Environment, 58, 21–44.CrossRefGoogle Scholar
  2. Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1–19.CrossRefGoogle Scholar
  3. BIS. (2002). Criteria for earthquake resistant design of structures: General provisions and buildings. In Bureau of Indian Standards (BIS) IS 1893:2002 (Part-1).Google Scholar
  4. Chalkias, C., Ferentinou, M., & Polykretis, C. (2014). GIS based landslide susceptibility mapping on the Peloponnese, Peninsula, Greece. Geosciences, 4, 176–190.  https://doi.org/10.3390/geosciences4030176.CrossRefGoogle Scholar
  5. Chawla, S., Chawla, A., and Pasupuleti, S. (2017). A feasible approach for landslide susceptibility map using GIS. Geo-Risk, ASCE, June 4–6, 2017, Denver, Colorado, https://ascelibrary.org/doi/10.1061/9780784480717.010 (pp. 101–110).
  6. Chawla, A., Chawla, S., Pasupuleti, S., Rao, A. C. S., Sarkar, K., & Dwivedi, R., (2018). Landslide susceptibility mapping in Darjeeling Himalayas, India. Advances in Civil Engineering, Natural Hazard Challenges to Civil Engineering Special Issue, Hindawi, Article ID 6416492 (pp. 1-17).  https://doi.org/10.1155/2018/6416492.
  7. Cruden, D. M. (1991). A simple definition of a landslide. Bulletin of the International Association of Engineering Geology, 43, 27–29.CrossRefGoogle Scholar
  8. Davis, J. C. (1986). Statistics and data analysis in geology (p. 646). New York: Wiley.Google Scholar
  9. Dorigo, M., & Birattari, M., (2011). Ant colony optimization. In Encyclopedia of machine learning (pp. 36–39). Boston, MA: Springer.Google Scholar
  10. Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.CrossRefGoogle Scholar
  11. 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&4), 229–250.CrossRefGoogle Scholar
  12. Gupta, R. P. (2003). Remote sensing geology (2nd ed.). Berlin: Springer.CrossRefGoogle Scholar
  13. Gupta, R. P., & Joshi, B. C. (1990). Landslide hazard zonation using the GIS approach—A case study from the Ramganga Catchment, Himalayas. Eng. Geology, 28, 119–131.CrossRefGoogle Scholar
  14. Gupta, S. K., Shukla, D. P., & Thakur, M. (2018). Selection of Weightages for causative factors used in the preparation of landslide susceptibility zonation (LSZ). Journal of Geomatics, Natural Hazards and Risk, 9(1), 471–487.CrossRefGoogle Scholar
  15. Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85, 347–366.  https://doi.org/10.1016/j.enggeo.2006.03.004.CrossRefGoogle Scholar
  16. Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2009). Landslide susceptibility zonation (LSZ) mapping—A review. Journal of South Asia Disaster Studies, 2(1), 81–105.Google Scholar
  17. Kanwal, S., Atif, S., & Shafiq, M. (2016). GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of shigar and shyok basins. Geomatics, Natural Hazards and Risk.  https://doi.org/10.1080/19475705.2016.1220023.CrossRefGoogle Scholar
  18. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942–1948.CrossRefGoogle Scholar
  19. Nagarajan, R., Mukherjee, A., Roy, A., & Khire, M. V. (1998). Temporal remote sensing data and GIS application in landslide hazard zonation of part of Western Ghat, India. International Journal of Remote Sensing, 19, 573–585.CrossRefGoogle Scholar
  20. National Atlas & Thematic Mapping Organisation. (2015). District Planning map series of Darjeeling, West Bengal.Google Scholar
  21. Nguyen, H. V., & Bai, L. (2010). Cosine similarity metric learning for face verification (pp. 709–720). Berlin, Heidelberg: Springers.Google Scholar
  22. Paul, C., & Ghoshal, T. B., (2009). An inventory of major landslides in Sikkim-Darjeeling Himalaya. In Geological Survey of India, special publication no. 94.Google Scholar
  23. Sarkar, S. (1996) Landslide hazard zonation and slope stability assessment techniques: Applications to Srinagar-Rudraprayag, Garhwal Himalaya. Ph.D. thesis, University of Roorkee, India, p 156.Google Scholar
  24. Sarkar, S., & Kanungo, D. P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5), 617–625.CrossRefGoogle Scholar
  25. Sarkar, S., Roy, A. K., & Martha, T. R. (2013). Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. Journal Geological Society of India, 82, 351–362.CrossRefGoogle Scholar
  26. Schuster, R. (1996). Socioeconomic significance of landslides. In A. K. Turner & R. L. Schuster (Eds.), Landslides: Investigation and Mitigation: Special report (Vol. 247, pp. 12–36)., National Academic Press DC: Washington.Google Scholar
  27. Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific Reports, 5(9899), 1–14.  https://doi.org/10.1038/srep09899.CrossRefGoogle Scholar
  28. Sinha, B. N., Varma, R. S., & Paul, D. K. (1975). Landslides in Darjeeling district (West Bengal) and adjacent areas. Bulletins of the Geological Survey of India, Series A, 36, 1–45.Google Scholar
  29. Summerfield, M. A. (1991). Global. Geomorphology, Pearson, 537.Google Scholar
  30. Taib, S. N. L., Selaman, O. S., Chen, C. L., Lim, R., & Awang Ismail, D. S. (2017). Landslide susceptibility in relation to correlation of groundwater development and ground condition. Advances in Civil Engineering, Hindawi, Article ID: 4320340.  https://doi.org/10.1155/2017/4320340.CrossRefGoogle Scholar
  31. Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G., & Vandekerckhove, L. (2006). Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology, 76(3–4), 392–410.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  3. 3.Department of Applied GeologyIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  4. 4.Department of Computer Science and EngineeringVignan’s Foundation for Science, Technology and ResearchVadlamudiIndia

Personalised recommendations