Skip to main content
SpringerLink
Log in

Landslide susceptibility assessment in Sikkim Himalaya with RS & GIS, augmented by improved statistical methods

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Landslide susceptibility zonation is a widely studied method for assessing the likelihood of landslides in specific areas. This study focuses on the Ranikhola watershed in the Sikkim Himalaya and utilizes the Frequency Ratio (FR) and Modified Information Value (MIV) methods to analyse landslide susceptibility. To enhance the susceptibility mapping a novel approach for the FR and MIV is introduced where the factor classes of higher importance were utilized. The study further evaluates a methodology that incorporates weighted ranking of landslide conditioning factor classes using FR and MIV indexes to generate landslide susceptibility maps (LSM). The landslide inventory comprises 124 landslides identified through satellite imagery from Q-GIS quick maps, ESRI base map, Google Earth, and Sentinel 2 A & B. Sixteen conditioning factors are considered, including elevation, slope angle, aspect, curvature, drainage characteristics, vegetation index, geology, soil type, rainfall, road density, and land use. The LSI and LSM are derived from these factors. The LSM created using traditional FR and MIV methods show that 9.55% and 5.96% of the watershed area fall within the High Susceptibility Zone (HSZ) and Very High Susceptibility Zone (VHSZ), respectively. However, the novel approach reveals that 11.54% and 10.29% of the study area fall within the HSZ and VHSZ. The weighted ranking method indicates that 16.22% of the Ranikhola watershed area is within the HSZ and VHSZ. The models are evaluated using the area under the receiver operating characteristic curve (AUC), with FR and MIV methods producing AUC values of 0.77 and 0.68, respectively. The new approach improves the AUC of the MIV method to 0.76, while the FR method remains relatively unchanged. The weighting method outperforms other FR and MIV methods, with an AUC of 0.90. Correlation analysis of the condition factors suggests that profile curvature, slope, stream power index, and topographic wetness index are the most influential factors, positively impacting each other and contributing to higher landslide susceptibility. The study emphasizes the importance of incorporating weighted ranking of landslide conditioning factor classes to create LSM, rather than relying on the total landslide susceptibility index (LSI) of factors. The findings provide valuable data for future large-scale investigations and efforts to enhance hazard preparedness in the Ranikhola watershed.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Anbalagan R, Kumar R, Lakshmanan K, Parida S, Neethu S (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley. Sikkim Geoenviron Disasters 2(1):6. https://doi.org/10.1186/s40677-014-0009-y

    Article  Google Scholar 

  • Avinash KG, Ashamanjari KG (2010) A GIS and frequency ratio based landslide susceptibility mapping: Aghnashini river catchment, Uttara Kannada, India. Int J Geomatics Geosci 1(3):343–354

    Google Scholar 

  • Bera A, Mukhopadhyay BP, Das D (2019) Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: A case study from Eastern Himalayas, Namchi. South Sikkim Natural Hazards 96(2):935–959. https://doi.org/10.1007/s11069-019-03580-w

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrolog Sci J 24(1):43–69

  • Bhasin R, Aslan G, Dehls J (2023) Ground investigations and detection and monitoring of landslides using SAR Interferometry in Gangtok, Sikkim Himalaya. Geohazards 4(1):25–39

    Article  Google Scholar 

  • 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 

  • Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Das B, Roy P (2020) Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci 79(20):476. https://doi.org/10.1007/s12665-020-09227-5

    Article  Google Scholar 

  • Chowdhuri I, Pal SC, Chakrabortty R, Malik S, Das B, Roy P, Sen K (2021) Spatial prediction of landslide susceptibility using projected storm rainfall and land use in Himalayan region. Bull Eng Geol Env 80(7):5237–5258. https://doi.org/10.1007/s10064-021-02252-z

    Article  Google Scholar 

  • Dutta K, Wanjari N, Misra AK (2023) Stability analysis of road cut slopes in Sikkim Himalaya along national highway 10, India. Geol Ecol Landsc 1–14

  • Egan JP (1975) Signal detection theory and ROC analysis. Academic Press, New York 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

    Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  Google Scholar 

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3):85–98. https://doi.org/10.1016/j.enggeo.2008.03.022

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415

    Article  Google Scholar 

  • GSI, Bhukosh dataset (2022) Geological Survey of India. Government of India, Kolkata. https://bhukosh.gsi.gov.in/Bhukosh/Public

  • Gupta N, Pal SK, Das J (2022a) GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya. Ann GIS 28(3):359–384. https://doi.org/10.1080/19475683.2022.2040587

  • Gupta V, Kumar S, Hermanns R, Penna I, Dehls J, Sengupta A, Bhasin RK (2022b) Spatial prediction of landslide susceptibility zones using Artificial Neural Network in the Sikkim Himalaya, India. In EGU General Assembly Conference Abstracts (pp EGU22–11496)

  • Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580

    Article  Google Scholar 

  • Hutchinson JN (1995) Keynote paper: landslide hazard assessment. In: Bell (ed) Landslides. Balkema, Rotterdam, pp 1805–1841

  • Intarawichian N, Dasananda S (2011) Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environ Earth Sci 64:2271–2285

    Article  Google Scholar 

  • Kanungo D, Mehrotra GS, Sarkar S, Mahadeviah K (1996) Terrain analysis and spatial assessment of landslide hazard in parts of Sikkim Himalaya. J Geol Soc India 47:491–498

    Google Scholar 

  • Karim S, Jalileddin S, Ali MT (2011) Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle-East J Sci Res 9(5):578–583

    Google Scholar 

  • Kaur H, Gupta S, Parkash S, Thapa R, Gupta A, Khanal GC (2019) Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques. Ann GIS 25(2):113–132. https://doi.org/10.1080/19475683.2019.1575906

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequencyratio and logistic regression models. Landslides 4(1):33–41

    Article  Google Scholar 

  • Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990

    Article  CAS  Google Scholar 

  • Mandal S, Mandal K (2018a) Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya India. Spatial Inf Res 26(1):59–75. https://doi.org/10.1007/s41324-017-0156-9

    Article  Google Scholar 

  • Mandal S, Mandal K (2018b) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Model Earth Syst Environ 4(1):69–88. https://doi.org/10.1007/s40808-018-0426-0

    Article  Google Scholar 

  • Mandal S, Mondal S (2019) Statistical approaches for landslide susceptibility assessment and prediction, vol 27. Springer

    Google Scholar 

  • Mandal K, Saha S, Mandal S (2021) Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya. India Geoscience Frontiers 12(5):101203. https://doi.org/10.1016/j.gsf.2021.101203

    Article  Google Scholar 

  • Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ramakrishnan D, Ghose MK, Chandran RV, Jeyaram A (2005) Probabilistic Techniques, GIS and Remote Sensing in Landslide Hazard Mitigation: A Case Study from Sikkim Himalayas, India. Geocarto Int 20(4):53–58. https://doi.org/10.1080/10106040508542364

    Article  Google Scholar 

  • Saha A, Mandal S, Saha S (2020) Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods. SN Appl Sci 2(10):1647. https://doi.org/10.1007/s42452-020-03441-3

    Article  Google Scholar 

  • Saha S, Roy J, Pradhan B, Hembram TK (2021) Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India. Adv Space Res 68(7):2819–2840. https://doi.org/10.1016/j.asr.2021.05.018

    Article  Google Scholar 

  • Sarkar S, Kanungo D, Patra A, Kumar P (2006) GIS based landslide susceptibility mapping—a case study in indian himalaya, disaster mitigation of debris flows, slope failures and landslides, pp. 617–624, Universal Academy Press, Inc. / Tokyo, Japan. https://www.researchgate.net/profile/D-Kanungo/publication/242073557_GIS_Based_Landslide_Susceptibility_Mapping_-_A_Case_Study_in_Indian_Himalaya/links/0c960528c38a2b19b4000000/GIS-Based-Landslide-Susceptibility-Mapping-A-Case-Study-in-Indian-Himalaya.pdf

  • Sarkar S, Kanungo DP, Patra AK, Kumar P (2008) GIS based spatial data analysis for landslide susceptibility mapping. J Mt Sci 5(1):52–62. https://doi.org/10.1007/s11629-008-0052-9

    Article  Google Scholar 

  • Sharma LP, Patel N, Ghose MK, 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 Int 29(2):128–146. https://doi.org/10.1080/10106049.2012.748830

    Article  Google Scholar 

  • Sonker I, Tripathi JN, Singh AK (2021) Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya. Quat Sci Adv 4:100039. https://doi.org/10.1016/j.qsa.2021.100039

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293. https://doi.org/10.1126/science.3287615

  • Tehrani FS, Calvello M, Liu Z, Zhang L, Lacasse S (2022) Machine learning and landslide studies: recent advances and applications. Nat Hazards 114(2):1197–1245

    Article  Google Scholar 

  • Valdiya KS (2015) The making of India: geodynamic evolution. Springer

    Google Scholar 

  • Varnes DJ (1984) Landslide Hazard Zonation: A review of principles and practice, Commission on Landslides of the IAEG. UNESCO Natural Hazards 3:61

    Google Scholar 

  • Wang Q, Wang D, Huang Y, Wang Z, Zhang L, Guo Q, Chen W, Chen W, Sang M (2015) Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment. Sustainability 7(12):16653–16669. https://doi.org/10.3390/su71215839

    Article  Google Scholar 

  • Williams CJ, Lee SS, Fisher RA, Dickerman LH (1999) A comparison of statistical methods for prenatal screening for Down syndrome. Appl Stochastic Models Data Anal 15:89–101

    Google Scholar 

  • Zhang T, Li Y, Wang T, Wang H, Chen T, Sun Z, Han L (2022) Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping. Geoscience Letters 9(1):1–16

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude for the research facilities provided by the Department of Geology, Sikkim University, which were instrumental in conducting the study described in this manuscript.

Author information

Authors and Affiliations

  1. Department of Geology, School of Physical Sciences, New Science Block, Sikkim University (A Central University), Near Ayurveda Hospital, Garigaon, Tadong, Gangtok, Sikkim, 737102, India

    Kuldeep Dutta, Nishchal Wanjari & Anil Kumar Misra

Authors
  1. Kuldeep Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nishchal Wanjari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anil Kumar Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Kuldeep Dutta played a pivotal role in collecting field data, processing & interpretation of GIS & RS data and developing the framework for the study, while Nishchal Wanjari and Anil Kumar Misra provided valuable assistance in data interpretation and manuscript writing. The authors confirm that all co-authors have thoroughly reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Anil Kumar Misra.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

Authors do not have any financial and non-financial competing interests reported in the manuscript.

Additional information

Responsible Editor: Zeynal Abiddin Erguler

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, K., Wanjari, N. & Misra, A.K. Landslide susceptibility assessment in Sikkim Himalaya with RS & GIS, augmented by improved statistical methods. Arab J Geosci 17, 138 (2024). https://doi.org/10.1007/s12517-024-11944-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-024-11944-1

Keywords

Search

Navigation