Skip to main content

Advertisement

SpringerLink
Log in

A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Landslide susceptibility map provides a useful tool to the decision-makers to prevent and mitigate landslide hazards. For this study 16 spatial parameters and past landslide inventory have been taken into consideration and these are categorized under six factors clusters. For providing relative importance to the parameters modified analytic hierarchy process is taken into consideration. Landslide susceptible zone (LSZ) is prepared compositing all those multiparametric spatial data layers. The obtained result shows that 7.80% area of total basin is highly susceptible for landslide. Correlation and regression analysis suggests that lithological factors cluster is the dominant one for determining very high LSZ. The validation shows that very high LSZ is associated with very high landslide frequency density. Besides this, receiver operating characteristics curve also shows 90.20% predicted area under the curve. So, this model can be treated as valid.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abdullah, T. O., Ali, S. S., Al-Ansari, N. A., & Knutsson, S. (2015). Groundwater vulnerability mapping using lineament density on standard drastic model: Case study in Halabja Saidsadiq Basin, Kurdistan Region, Iraq. Engineering,7, 644–667. https://doi.org/10.4236/eng.2015.710057.

    Article  CAS  Google Scholar 

  • Agriculture Contingency Plan for Darjeeling District. http://dac.gov.in/Admin_Agricoop/Uploaded_File/WestBengal%206-Darjeeling-31.12.2011.pdf.

  • Agriculture Contingency Plan for Jalpaiguri District. http://dac.gov.in/Admin_Agricoop/Uploaded_File/WestBengal%206-Jalpaiguri-31.12.2011.pdf.

  • Ahmed, M. F., Rogers, J. D., & Ismail, E. H. (2014). A regional level preliminary landslide susceptibility study of the upper Indus river basin. European Journal of Remote Sensing,47, 343–373.

    Google Scholar 

  • Akbari, A., Yahaya, F. B. M., Azamirad, M., & Fanodi, M. (2014). Landslide susceptibility mapping using logistic regression analysis and GIS tools. Electronic Journal of Geotechnical Engineering,19, 1687–1696.

    Google Scholar 

  • Akgun, A., & Turk, N. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) 379 and its vicinity by multicriteria decision analysis. Environmental Earth Sciences,61(3), 595–611.

    Google Scholar 

  • Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences,44, 120–135.

    Google Scholar 

  • Ansari, Md A, Khan, P. K., Tiwari, V. M., & Banerjee, J. (2014). Gravity anomalies, flexure, and deformation of the converging Indian lithosphere in Nepal and Sikkim-Darjeeling Himalayas. Int J Earth Sci (GeolRundsch),103, 1681–1697.

    CAS  Google Scholar 

  • Ayalew, L., Yamagishi, H., Marui, H., & Kanno, T. (2005). Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology,81, 432–445.

    Google Scholar 

  • Basu, T. & Pal, S. (2017a). Identification of landslide susceptibility zones in Gish River basin, West Bengal, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. https://doi.org/10.1080/17499518.2017.1343482

    Article  Google Scholar 

  • Basu, T. & Pal, S. (2017b). Exploring landslide susceptible zones by analytic hierarchy process (AHP) for the Gish River Basin, West Bengal, India. Spatial Information Research, 25(5), 1–10.

    Google Scholar 

  • Basu, T., & Pal, S. (2018). RS-GIS based morphometrical and geological multi-criteria approach to the landslide susceptibility mapping in Gish River Basin, West Bengal. India. Advances in space research,63(3), 1253–1269.

    Google Scholar 

  • Bhattacharya, S. K. (2012). Landslide disaster perception of the AILA cyclone in the Darjeeling town, West Bengal, India. International Journal of Geomatics and Geosciences,3(1), 21–27.

    Google Scholar 

  • Bhattacharya, S. K. (2013). The Study of Paglajhora Landslide in the Darjeeling Hills, West Bengal, India. Indian Journal of Spatial Science,4(1), 21–27.

    Google Scholar 

  • Bhusan, K., Kundu, S., Goswami, K., & Sudhakar, S. (2014). Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,40, 15–19. https://doi.org/10.5194/isprsarchives-XL-8-15-2014.

    Article  Google Scholar 

  • Biswas, S. S., & Pal, R. (2016). Causes of Landslides in Darjeeling Himalayas during June–July, 2015. Journal of Geography and Natural Disasters,6(2), 1–5. https://doi.org/10.4172/2167-0587.1000173.

    Article  Google Scholar 

  • Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Spatial prediction of landslide hazards in HoaBinh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena,96, 28–40. https://doi.org/10.1016/j.catena.2012.04.001.

    Article  Google Scholar 

  • Calligaris, C., Poretti, G., Tariq, S., & Melis, M. T. (2013). First steps towards a landslide inventory map of the Central Karakoram National Park. European Journal of Remote Sensing,46, 272–287.

    Google Scholar 

  • Chakraborty, I., Ghosh, S., Bhattacharya, D., & Bora, A. (2011). Earthquake induced landslides in the Sikkim-Darjeeling Himalayas—An aftermath of the 18th September 2011 Sikkim earthquake. Geological Society of India, 1–8.

  • Chamling, M. (2013). Landslides: A geographical review in and around Pagla Jhora Region of the Eastern Himalayan Belt of Darjeeling, West Bengal, Indian. Journal of Research,2(8), 1–3.

    Google Scholar 

  • Chauhan, S., Sharma, M., Arora, M. K., & Gupta, N. K. (2010). Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation,12, 340–350.

    Google Scholar 

  • Chowdhury, A., Madan, K., & Jha, C. V. M. (2010). Delineation of Groundwater recharge zones and identification of artificial recharge sites in West Medinipur district using RS, GIS and MCDM techniques. Environmental Earth Sciences,59, 1209–1222.

    Google Scholar 

  • Eastman, J. R. (2006). Idrisiandes: Tutorial, clark labs. Worcester: Clark University.

    Google Scholar 

  • Eshghabad, S. M., Solaimani, K., & Omidvar, E. (2012). Landslide susceptibility mapping using multiple regression and GIS tools in Tajan Basin, North of Iran. Environment and Natural Resources Research,2(3), 43–51.

    Google Scholar 

  • Feizizadeh, B., & Blaschke, T. (2011). Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. Journal of Earth Science Engineering,1, 66–71.

    Google Scholar 

  • Fowze, J. S. M., Thakar, V., Wahyu, S., Shofiyati, R., Hazarika, M. K., Samarakoon, L. (2010). Landslide susceptibility mapping in agricultural land using remote sensing and GIS: A case study in East Java, Indonesia (pp. 1–6). Asian Association on Remote Sensing.

  • Froehlich, W., Gil, E., Kasza, I., & Starkel, L. (1990). Thresholds in the transformation of slopes and river channels in the Darjeeling Himalaya, India. Mountain Research and Development,10, 301–312.

    Google Scholar 

  • Froehlich, W., & Starkel, L. (1987). Normal and extreme monsoon rains: their role in the shaping of the Darjeeling Himalaya. Studia Geomorphologica Carpatho-Balcanica,21, 129–156.

    Google Scholar 

  • Froude, M. J., & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth Systems Sciences,18, 2161–2181.

    Google Scholar 

  • Hadley, R. F., & Schumm, S. A. (1961). Sediment sources and drainage basin characteristics in upper Cheyenne River Basin. US Geol Survey Water Supply Paper,1531(B), 198.

    Google Scholar 

  • Iwahashi, J., Okatani, T., Nakano, T., Koarai, M., & Otoi, K. (2014). Landslide susceptibility analysis by terrain and vegetation attributes derived from pre-event LiDAR data: A case study of granitic mountain slopes in Hofu, Japan. INTERPRAEVENT2014 in the Pacific Rim November 25–28, 2014, in Nara, Japan.

  • Kaliraj, S., Chandrasekar, N., & Magesh, N. S. (2013). Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arabian Journal of Science. https://doi.org/10.1007/s12517-013-0849-x.

    Article  Google Scholar 

  • Kanungo, D. P., Sarkar, S., Sharma, S. (2011). Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Natural Hazards, 59, 1491–1512.

    Google Scholar 

  • Karsli, F., Atasoy, M., Yalcin, A., Reis, S., Demir, O., & Gokceoglu, C. (2009). Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey). Environmental Monitoring and Assessment,156, 241–255.

    CAS  Google Scholar 

  • Kitutu, M. G., Muwanga, A., Poesen, J., & Deckers, J. A. (2009). Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda. African Journal of Agricultural Research,4(7), 611–620.

    Google Scholar 

  • Lee, S., & Lee, M. J. (2006). Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea. Advances in Space Research,38, 2261–2271.

    Google Scholar 

  • Malczewski, J. (1999). GIS and multi-criteria decision analysis. New York: JohnWiley and Sons.

    Google Scholar 

  • Mandal, S., & Maiti, R. (2013). Assessing the triggering rainfall-induced landslip events in the Shivkholawatershed Of Darjiling Himalaya, West Bengal. European Journal of Geography,4(3), 21–37.

    Google Scholar 

  • Mandal, S., & Maiti, R. (2014). Role of lithological composition and lineaments in landsliding: A case study of Shivkhola Watershed, Darjeeling Himalaya. International Journal of Geology, Earth and Environmental Sciences,4(1), 126–132.

    Google Scholar 

  • Mandal, B., & Mandal, S. (2016). Assessment of mountain slope instability in the Lish River basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM). Modeling Earth Systems and Environment,2, 1–14.

    Google Scholar 

  • Melton, M. A. (1957). An analysis of the relation among elements of climate, surface properties, and geomorphology. Office of Naval Research Technical Report No. 11, contract N60NR271-30, New York, Columbia University.

  • Moore, I. D., & Burch, G. J. (1986). Physical basis of the length-slope factor in the universal soil loss equation. Soil Science Society of America Journal,50, 1294–1298.

    Google Scholar 

  • Moore, I. D., Gessler, P. E., Nielsen, G. A., & Peterson, G. A. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal,57, 443–452.

    Google Scholar 

  • Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes,5, 3–30.

    Google Scholar 

  • Mor, S. (2013). Critical ecosystem modeling and analysis of Darjeeling District, West Bengal, India using geospatial techniques (p. 102). Bengaluru: Forestry and Ecology Department, ISRO.

    Google Scholar 

  • Moradi, M., Bazyar, M. H., & Mohammadi, Z. (2012). GIS-based landslide susceptibility mapping by AHP method, A case study, Dena City, Iran. Journal of Basic and Applied Scientific Research,2(7), 6715–6723.

    Google Scholar 

  • Moradi, S., & Rezaei, M. (2014). A GIS-based comparative study of the analytic hierarchy process, bivariate statistics and frequency ratio methods for landslide susceptibility mapping in part of the Tehran metropolis, Iran. JGeope,4(1), 45–61.

    Google Scholar 

  • Nefeslioglu, H. A., Duman, T. Y., & Durmaz, S. (2008). Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology,94, 401–418.

    Google Scholar 

  • Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences,37, 1264–1276.

    Google Scholar 

  • Omosanya, K. O., Mosuro, G. O., Laniyan, T. A., & Ogunleye, D. (2012). Prediction of gravity anomaly from calculated densities of rocks. Advances in Applied Science Research,3(4), 2059–2068.

    Google Scholar 

  • Ortiz, J. A. V., & Martínez-Graña, A. M. (2018). A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomatics, Natural Hazards and Risk, 9(1), 1106–1128.

    Google Scholar 

  • Paul, P. K., & Das, S. (2014). Landslide susceptibility evaluation of GIT watershed of Kalimpong Subdivision, India using information value method. International Journal of Economic and Environment Geology,5(2), 18–24.

    Google Scholar 

  • Peduzzi, P. (2010). Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach. Natural Hazards and Earth Systems Sciences,10, 623–640.

    Google Scholar 

  • Pourghasemi, H. R., Moradi, H. R., FatemiAghda, S. A., Mahdavifar, M. R., & Mohammdi, M. (2009). Landslide hazard assessment using fuzzy multi criteria decision-making method. Iranian Jornal of Watershed Management Science & Engineering,3(8), 51–62.

    Google Scholar 

  • Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., & Moezzi, K. D. (2012). Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. Terrigenous mass movements (pp. 23–49). Berlin: Springer. https://doi.org/10.1007/978-3-642-25495-6_2.

    Book  Google Scholar 

  • Pradhan, B. (2012). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences,51, 350–365.

    Google Scholar 

  • Qin, C. Z., Zhu, A. X., Pei, T., Li, B. L., Scholten, T., Behrens, T., et al. (2009). An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agriculture,12(1), 32–43.

    Google Scholar 

  • Rai, P. K., Mohan, K., & Kumra, V. K. (2014). Landslide hazard and its mapping using remote sensing and GIS. Journal of Scientific Research,58, 1–13.

    Google Scholar 

  • Rasyid, A. R., Bhandary, N. P., & Ryuichi, Y. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters,3(19), 1–16.

    Google Scholar 

  • Reichenbach, P., Busca, C., Mondini, A. C., & Rossi, M. (2014). The influence of land use change on landslide susceptibility zonation: The Briga Catchment Test Site (Messina, Italy). Environmental Management,54, 1372–1384.

    CAS  Google Scholar 

  • Rhoad, R., Milauskas, G., & Whipple, R. (1991). Geometry for enjoyment and challenge. Littell: McDougal.

    Google Scholar 

  • Rostami, Z. A., Al-modaresi, S. A., Fathizad, H., & Faramarzi, M. (2016). Landslide susceptibility mapping by using fuzzy logic: a case study of Cham-gardalan catchment, Ilam, Iran. Arabian Journal of Geosciences,9(17), 1–11.

    Google Scholar 

  • Saadatkhah, N., Kassim, A., & Lee, L. M. (2014). Qualitative and quantitative landslide susceptibility assessments in Hulu Kelang area, Malaysia. EJGE,19, 545–563.

    Google Scholar 

  • Saaty, T. L. (1980). The analytical hierarchy process. New York: McGraw Hill.

    Google Scholar 

  • Saaty, T. L. (1990). Remarks on the analytic hierarchy process. ManagSci,36(3), 259–268.

    Google Scholar 

  • Sdao, F., Lioi, D. S., Pascale, S., Caniani, D., & Mancini, I. M. (2013). Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Natural Hazards and Earth Systems Sciences,13, 395–407.

    Google Scholar 

  • Shadman, M., Rahimi, S., & Jafar Beglou, M. (2014). PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Natural Hazards,73, 77–95.

    Google Scholar 

  • Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific Reports,5, 9899. https://doi.org/10.1038/srep09899.

    Article  CAS  Google Scholar 

  • Shit, P. K., Bhunia, G. S., & Maiti, R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment,2, 21. https://doi.org/10.1007/s40808-016-0078-x.

    Article  Google Scholar 

  • Simon, N., Roslee, R., Marto, N. L., Akhir, J. M., Rafek, A. G., & Lai, G. T. (2014). Lineaments and their association with landslide occurrences along the Ranau-Tambunan Road, Sabah. EJGE,19, 645–655.

    Google Scholar 

  • Skilodimou, H. D., Bathrellos, G. D., et al. (2018). Physical and anthropogenic factors related to landslide activity in the northern Peloponnese. Land, 85(7), 1–18.

    Google Scholar 

  • Strahler, A. N. (1964). Quantitative geomorphology of drainage basins and channel networks. In V. T. Chow (Ed.), Handbook of applied hydrology (pp. 411–476). New York: McGraw-Hill.

    Google Scholar 

  • Sujatha, E. R., & Rajamanickam, G. V. (2012). Analysis of Landslide Susceptibility to Different Land Use Patterns in a Part of Kodaikkanal, Tamilnadu. Bonfring International Journal of Industrial Engineering and Management Science,2(1), 53–55.

    Google Scholar 

  • Sujatha, E. R., Rajamanickam, G. V., & Kumaravel, P. (2012). Landslide susceptibility analysis using probabilistic certainty factor approach: A case study on Tevankarai stream watershed, India. Journal of Earth System Science,121(5), 1337–1350.

    Google Scholar 

  • Suzen, M. L., & Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchments, Turkey. Engineering Geology,71, 303–321.

    Google Scholar 

  • The Times of India, July 1, 2015. http://timesofindia.indiatimes.com/india/Heavy-rainfall-in-Darjeeling-trigger-landslides-36-killed/articleshow/47901318.cms.

  • Wang, L., Sawada, K., & Moriguchi, S. (2011). Landslide susceptibility mapping by using logistic regression model with neighborhood analysis: A case study in Mizunami City. Int. J. of Geomate,1(2), 99–104.

    CAS  Google Scholar 

  • Yazdadi, E. A., & Ghanavati, E. (2016). Landslide hazard zonation by using (analytic hierarchy process) model in GIS (geographic information system) environment (case study: Kordan Watershed). IJPSAT,2(1), 24–39.

    Google Scholar 

  • Yilmaz, C., Topal, T., & Suzen, M. L. (2011). GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences,65, 2161–2178.

    Google Scholar 

  • Yu, C., Liu, K., Meng, W., Wu, Z., & Rishe, N. (2002). A methodology for retrieving text documents from multiple databases. IEEE TKDE,14(6), 1347–1361.

    Google Scholar 

  • Zhuang, J., Peng, J., et al. (2016). Assessment and mapping of slope stability based on slope units: A case study in Yan’an, China. Journal of Earth System Science,125(7), 1439–1450. https://doi.org/10.1007/s12040-016-0741-7.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Gour Banga, Mokdumpur, Malda, West Benal, 732103, India

    Tirthankar Basu & Swades Pal

Authors
  1. Tirthankar Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swades Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tirthankar Basu.

Ethics declarations

Conflict of interest

All the authors conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 40 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basu, T., Pal, S. A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India. Environ Dev Sustain 22, 4787–4819 (2020). https://doi.org/10.1007/s10668-019-00406-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-019-00406-4

Keywords

Search

Navigation