Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model

  • Original Paper
  • Published:
Applied Geomatics Aims and scope Submit manuscript

Abstract

The assessment of landslide susceptibility is closely associated with the spatial distribution of previous landslides and landslide causative factors. In the present study, index of entropy (IOE) model was applied to map landslide susceptibility zones of Darjeeling Himalaya. This model is dealt with the relationship between landslide phenomena and landslide causative factors. To perform the model, 15 landslide conditioning data layers, i.e. elevation, aspect, slope, curvature, geology, soil, lineament density, distance to lineament, drainage density (DD), distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalised differential vegetation index (NDVI), and land use and land cover (LULC) were considered. Each and every class of landslide causative factor contributes a relative importance in landslide occurrences. To obtain the relative significance of each class of landslide causative factors and individual factor weight, Pij and Wj values of IOE model were estimated respectively to prepare landslide susceptibility map of Darjeeling Himalaya in GIS platform. The results suggested that the most important factor of landslide occurrences was soil type, whereas NDVI played least role in landslide susceptibility assessments. The prediction accuracy of the prepared landslide susceptibility map using ROC curve was 78.2%. Estimated frequency ratio value increased from very low to very high susceptibility zones, which showed the reliability and authenticity of IOE model.

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

Similar content being viewed by others

References

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34

    Article  Google Scholar 

  • Alvioli M, Marchesini M, Reichenbach P, Rossi M, Ardizzone F, Fiorucci F, Guzzetti F (2016) Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modelling. Geosci Model Dev 9:3975–3991. https://doi.org/10.5194/gmd-9-3975-2016

    Article  Google Scholar 

  • Anagnostopoulos GG, Fatichi S, Burlando P (2015) An advanced process-based distributed model for the investigation of rainfall-induced landslides: the effect of process representation and boundary conditions. Water Resour Res 51(9):7501–7523. https://doi.org/10.1002/2015WR016909

    Article  Google Scholar 

  • Basu T, Pal S (2017b) Identification of landslide susceptibility zones in Gish River basin, West Bengal, India. Georisk: Assess Manag Risk Eng Syst Geohazards. https://doi.org/10.1080/17499518.2017.1343482, 12,14,28

  • Basu T, Pal PS (2017a) Exploring landslide susceptible zones by analytic hierarchy process (AHP) for the Gish River Basin, West Bengal, India, 2017 Spat. Inf Res. https://doi.org/10.1007/s41324-0170134-2

  • Basu AR (1970) A geotechnical note on investigations of certain stretches of Border roads under Swastik Project in Sikkim and Darjeeling District, West Bengal, Unpublished GSI Report, FS 1969–1970

  • Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralˇovany–Liptovsky´ Mikuláš railway case study. Phys Chem Earth 35:162–171

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Bhandari RK (2007) The Indian landslide scenario: strategic issues and action points. Disast Dev 1(2)

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45:55–72

    Article  Google Scholar 

  • Chen W, Li W, Hou E (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arab J Geosci 7:4499–4511

    Article  Google Scholar 

  • Cheng TA, Lateh H, Peng KS (2008) Intelligence explanation system on landslide dissemination: a case study in Malaysia. Proceedings of the First World Landslide Forum Report: Implementing the 2006 Tokyo Action Plan on the International Program on Landslides (IPL): 330–333

  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011

    Article  Google Scholar 

  • Dempster AP (1968) A generalization of Bayesian inference. J R Stat Soc Ser B 30:205–247

    Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards 65(1):135–165. https://doi.org/10.1007/s11069-012-0347-6

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Felicísimo A, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189. https://doi.org/10.1007/s10346-012-0320-1

    Article  Google Scholar 

  • Gupta RP, Joshi BC (1990) Landslide hazard zonation using the GIS approach—a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999b) Landslide hazard evaluation: an aid to a sustainable development. Geomorphology 31:181–216

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999a) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216

    Article  Google Scholar 

  • Hansen A, Franks CAM, Kirk PA, Brimicombe AJ, Tung F (1995) Application of GIS to hazard assessment, with particular reference to landslides in Hong Kong. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic Publisher, Dordrecht, The Netherlands, pp 135–175

    Google Scholar 

  • Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281

    Article  Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926. https://doi.org/10.1007/s13762-013-0464-0

    Article  Google Scholar 

  • 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 85:347–366

    Article  Google Scholar 

  • Kausar N, Majid A (2016) Random forest-based scheme using feature and decision levels information for multi-focus image fusion. Pattern Anal Applic 19(1):221–236

    Article  Google Scholar 

  • Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43:120–131

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Ma F, Wang J, Yuan R, Zhao H, Guo J (2013) Application of analytical hierarchy process and least-squares method for landslide susceptibility assessment along the Zhong-Wu natural gas pipeline, China. Landslides 10(4):481–492

    Article  Google Scholar 

  • Mandal S, Maiti R (2014) Role of lithological composition and lineaments in landsliding: a case study of Shivkhola Watershed, Darjeeling Himalaya. Int J Geol Earth Environ Sci 4(1):126–132

    Google Scholar 

  • Mandal S, Mandal K (2018) Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya, India. Spat Inf Res 26:59–75. https://doi.org/10.1007/s41324-017-0156-9

  • Mezughi TH, Akhir JM, Rafek A, Abdullah I (2011) Landslide assessment using frequency ratio model applied to an area along the E-W highway (Gerik-Jeli). Am J Environ Sci 7(1):43–50

    Article  Google Scholar 

  • Mondal S, Mandal S (2017a) RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 12(1):29–44. https://doi.org/10.1080/17499518.2017.1347949

    Google Scholar 

  • Mondal S, Mandal S (2017b) Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya. Spat. Inf. Res. 25:337–350. https://doi.org/10.1007/s41324-017-0101-y

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29:5217–5236

    Article  Google Scholar 

  • NDMA (2009) Management of landslides and snow avalanches, 2009. National Disaster Management Authority (NDMA), Government of India, New Delhi, p 144

    Google Scholar 

  • Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the Metropolitan area of Istanbul, Turkey. Math Probl Eng 2010:1–15. https://doi.org/10.1155/2010/901095

    Article  Google Scholar 

  • O’leary, D. W., Friedman, J. D and Pohn, H. A. (1976). Lineament, linear, lineation: Sme proposed new standards for old terms. Geol Society Am Bull, 87. PP1463–1469

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464

    Article  Google Scholar 

  • Pham BT, Bui DT, Pourghasemi HR, Indra P, Dholakia M (2017) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan M (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Ramesh V, Anbazhagan S (2014) Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 73:8009–8021. https://doi.org/10.1007/s12665-014-3954-6

    Article  Google Scholar 

  • Ren LC (2000) Disaster entropy: conception and application. J Nat Disast 9(2):26–31

    Google Scholar 

  • Sang K (2013) Landslide disaster data statistics and analysis in China during nearly 60 years. Sci Technol Commun 10:109

    Google Scholar 

  • Sarkar S et al. (2010) Geo-hazards in sub Himalayan North Bengal, Department of Geography & Applied Geography, University of North Bengal

  • Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Int J Photogrammetric Eng Remote Sensing 70:617–625

    Article  Google Scholar 

  • Sharma LP, Patel N, Ghose MK, Debnath P (2013) Synergistic application of fuzzy logic and geoinformatics for landslide vulnerability zonation—a case study in Sikkim Himalayas, India. Appl Geomat 5:271–284

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Technol J 27:379–423

    Article  Google Scholar 

  • Sidle RC, Pearce AJ, O'Loughlin CL (1985) Hillslope stability and land use, Water Resources Monogr., Vol. 11, 140 pp. American Geophys. Union, Washington, D.C.

    Book  Google Scholar 

  • Starkel L (1972) The role of catastrophic rainfall in the shaping of the relief of the lower Himalaya (Darjeeling Hills). Geogr Pol 21:103–160

    Google Scholar 

  • Starkel L, Basu SR, Rains (2000) Landslides and floods in the Darjiling Himalayas, New Delhi, Indian Science Academy, pp 1–168

  • Strahler AN (1964) Quantitative geomorphology of drainage basin and channel network. Handbook of Applied Hydrology: 39–76

  • Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci 121(5):1337–1350

    Article  Google Scholar 

  • Torkashvand AM, Irani A, Sorur J (2014) The preparation of landslide map by landslide numerical risk factor (LNRF) model and geographic information system (GIS). Egyptian J Remote Sens Space Sci 17:159–170

    Article  Google Scholar 

  • Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through an artificial neural network classifier. Nat Hazards 74(3):1–28

    Article  Google Scholar 

  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124–135

    Article  Google Scholar 

  • van Westen CJ (1997) Statistical landslide hazard analysis. In: Application guide, ILWIS 2.1 for Windows. ITC, Enschede, The Netherlands, pp 73–84

  • van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419

    Article  Google Scholar 

  • Wang Q, Li W, Yan S, Wu Y, Pei Y (2016) GIS based frequency ratio and index of entropy models to landslide susceptibility mapping (Daguan, China). Environ Earth Sci 75:780. https://doi.org/10.1007/s12665-016-5580-y

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287

    Article  Google Scholar 

  • Yang Z, Qiao J (2009) Entropy-based hazard degree assessment for typical landslides in the three gorges area, China. Environ Sci Eng 519–529

  • Youssef AM, Al-Kathery M, Pradhan B (2014) Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19:113–134. https://doi.org/10.1007/s12303-014-0032-8

    Article  Google Scholar 

  • Yufeng S, Fengxiang J (2009) Landslide stability analysis based on generalized information entropy. 2009 International Conference on Environmental Science and Information Application Technology, pp. 83–85

Download references

Acknowledgements

The authors would like to express their sincere thanks to Survey of India (SOI), Geological Survey of India (GSI), and National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) for providing necessary data, facilities, and support during the study period.

Author information

Authors and Affiliations

  1. Research Scholar, Department of Geography, University of Gour Banga, Malda, West Bengal, 732103, India

    Subrata Mondal

  2. Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, 743368, India

    Sujit Mandal

Authors
  1. Subrata Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sujit Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrata Mondal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, S., Mandal, S. Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model. Appl Geomat 11, 129–146 (2019). https://doi.org/10.1007/s12518-018-0248-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12518-018-0248-9

Keywords

Search

Navigation