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Landslide susceptibility mapping of the Tehri reservoir rim area using the weights of evidence method

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Abstract

This study was aimed to utilise important landslide causal factors for the delineation of the landslide susceptible area using the weights of evidence (WofE) method in the Tehri reservoir rim region on a macro scale. The Tehri reservoir extends up to 70 km and bounded by moderate to steep slopes. Landslide susceptibility mapping (LSM) is an essential measure for identifying the potentially unstable slopes bounding the reservoir. With the help of ancillary data, remote sensing imagery and a digital elevation model, 10 causative factors along with landslide inventory were extracted. Initially, the WofE model was applied to obtain the association between landslides and causative factors. The process gave the numerical estimate of correlation between landslides and causative factors by means of positive and negative correlation. Important factor attributes, potentially causing landslides, were identified based on high positive correlation values. Later, the posterior probability of landslide occurrence for each mapping unit was also computed using the WofE model. Posterior probability was divided into five relative susceptibility classes. Validation of the posterior probability map was carried out by using the prediction rate curve technique and a reasonable accuracy of 83% was achieved. LSM of the Tehri reservoir rim area implicates unplanned road construction and settlements coupled with the reservoir slope settlement process for the present degradation of the geo-environmental system in that region.

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References

  • Agterberg F P and Cheng Q 2002 Conditional independence test for weights of evidence modelling; Nat. Resour. Res. 11 249–255.

    Article  Google Scholar 

  • Akgun A and Erkan O 2016 Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: In an artificial reservoir area at Northern Turkey; Arab. J. Geosci. 9 1–15.

    Article  Google Scholar 

  • Akgun A, Dag S and Bulut F 2008 Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood frequency ratio and weighted linear combination models; Environ. Geol. 54(6) 1127–1143.

    Article  Google Scholar 

  • Anbalagan R 1992 Landslide hazard evaluation and zonation mapping in mountainous terrain; Eng. Geol. 32 269–277.

    Article  Google Scholar 

  • Anbalagan R, Kumar R, Lakshmanan K, Parida S and 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) 1–17, https://doi.org/10.1186/s40677-014-0009-y.

  • Arora M K, Das Gupta A S and Gupta R P 2004 An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas; Int. J. Rem. Sens. 25 559–572.

    Article  Google Scholar 

  • Bonham-Carter G F 1994 Geographic information system for geoscientists: Modelling with GIS; Pergamon, Oxford, 398p.

    Google Scholar 

  • Bonham-Carter G F, Agterberg F P and Wright D F 1989 Weights of evidence modelling: A new approach to mapping mineral potential; In: Statistical applications in the earth sciences (eds) Agterberg F P and Bonham-Carter G F, Geol. Survey Canada Paper 89(9) 171–183.

  • Chakraborty D and Anbalagan R 2008 Landslide hazard evaluation of road cut slopes along Uttarkashi–Bhatwari road, Uttaranchal Himalaya; J. Geol. Soc. India 71 115–124.

    Google Scholar 

  • Ciurleo M, Cascini L and Calvello M 2017 A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils; Eng. Geol. 223 71–81.

    Article  Google Scholar 

  • Dahal R K, Hasegawa S, Nonomura S, Yamanaka M, Masuda T and Nishino K 2008 GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping; Environ. Geol. 54(2) 314–324.

    Article  Google Scholar 

  • Das I, Stein A and Dadhwal V K 2012 Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models; Geomorphology 179 116–125.

    Article  Google Scholar 

  • Dobhal D P, Gupta A K, Mehta M and Khandelwal D D 2013 Kedarnath disaster: Facts and plausible causes; Curr. Sci. 105 171–174.

    Google Scholar 

  • ESRI F 2012 What is the Jenks optimization method? http://support.esri.com/en/knowledgebase/techarticles/detail/26442.

  • Fabbri A G and Chung C J 2008 On blind tests and spatial prediction models; Nat. Resour. Res. 17(2) 107–118.

    Article  Google Scholar 

  • Fritz H M, Phillips D A, Okayasu A, Shimozono T, Liu H, Fahad M, Skanavis V, Synolakis C E and Takahashi T 2012 The 2011 Japan tsunami current velocity measurements from survivor videos at Kesennuma Bay using LiDAR; Geophys. Res. Lett. 39 L00G23, https://doi.org/10.1029/2011GL050686.

    Article  Google Scholar 

  • Ghosh S, Van Westen C J, Carranza E J M, Ghoshal T B, Sarkar N K and Surendranath M 2009 A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility; J. Geol. Soc. India 74(5) 625–638.

    Article  Google Scholar 

  • Gupta P and Anbalagan R 1997 Landslide hazard zonation (LHZ) and mapping to assess slope stability of parts of the proposed Tehri dam reservoir, India; Quart. J. Eng. Geol. 30 27–36.

    Article  Google Scholar 

  • Gupta R P 2018 Remote sensing geology; Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-662-55876-8.

    Book  Google Scholar 

  • Gupta R P, Kanungo D P, Arora M K and Sarkar S 2008 Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps; Int. J. Appl. Earth Obs. Geoinf. 10 330–341.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hong H, Liu J, Tien Bui D, Pradhan B, Acharya T D, Pham B T, Zhu A X, Chen W and Bin Ahmad B 2018 Landslide susceptibility mapping using J48 decision tree with AdaBoost, bagging and rotation forest ensembles in the Guangchang area (China); Catena 163 399–413.

    Article  Google Scholar 

  • Kalantar B, Pradhan B, Naghibi S A, Motevalli A and Mansor S 2018 Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN); Geomat. Nat. Haz. Risk 9(1) 49–69.

    Article  Google Scholar 

  • Kannan M, Saranathan E and Anabalagan R 2013 Landslide vulnerability mapping using frequency ratio model: A geospatial approach in Bodi–Bodimettu Ghat section, Theni district, Tamil Nadu, India; Arab. J. Geosci. 6(8) 2901–2913.

    Article  Google Scholar 

  • Kanungo D P, Arora M K, Sarkar S and 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; Eng. Geol. 85 347–366.

    Article  Google Scholar 

  • Kayastha P, Dhital M R and De Smedt F 2012 Landslide susceptibility mapping using weight of evidence in the Tinau watershed, Nepal; Nat. Hazards 63(2) 479–498.

    Article  Google Scholar 

  • Kirschbaum D, Adler R, Hong Y, Hill S and Lerner-Lam A 2010 A global landslide catalog for hazard application: Method, result, and limitations; Nat. Hazards 52 561–575.

    Article  Google Scholar 

  • Kouli M, Loupasakis C, Soupios P, Rozos D and Vallianatos F 2013 Comparing multi-criteria methods for landslide susceptibility mapping in Chania Prefecture, Crete Island, Greece; Nat. Hazards Earth Syst. Sci. Discuss. 1 73–109.

    Article  Google Scholar 

  • Kumar R and Anbalagan R 2015 Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS; J. Earth Syst. Sci. 124(2) 431–448.

    Article  Google Scholar 

  • Kumar R and Anbalagan R 2016 Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand; J. Geol. Soc. India 87(3) 271–286.

    Article  Google Scholar 

  • Kumar D, Thakur M, Dubey C S and Shukla D P 2017 Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India; Geomorphology 295 15–125.

    Article  Google Scholar 

  • Kundu S, Saha A K, Sharma D C and Pant C C 2013 Remote sensing and GIS based landslide susceptibility assessment using binary logistic regression model: A case study in the ganeshganga watershed, Himalayas; J. Indian Soc. Rem. Sens. 41(3) 697–709.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mathew J, Jha V K and Rawat G S 2007 Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand; Curr. Sci. 92(5) 628–638.

    Google Scholar 

  • Mihalasky M J 1999 Mineral potential modelling of gold and silver mineralization in the Nevada Great Basin: A GIS-based analysis using weights of evidence; Unpublished Doctoral Dissertation, Univ. Ottawa.

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

    Article  Google Scholar 

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

  • OFDA/CRED 2010 EM-DAT International disaster database – http://www.em-dat.net; Universite Catholique de Louvain, Brussels, Belgium.

  • Pardeshi S D, Autade S E and Pardeshi S S 2013 Landslide hazard assessment: Recent trends and techniques; Springerplus 523(2) 1–11.

    Google Scholar 

  • Porwal A, Carranza E J M and Hale M 2003 Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping; Nat. Resour. Res. 12 1–25.

    Article  Google Scholar 

  • Pourghasemi H R, Pradhan B and Gokceoglu C 2012 Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran; Nat. Hazards 63(2) 965–996.

    Article  Google Scholar 

  • Pradhan B 2010 Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches; J. Indian Soc. Rem. Sens. 38(2) 301–320.

    Article  Google Scholar 

  • Pradhan B, Hyon-Joo Oh and Buchroithner M 2010 Use of remote sensing data and GIS to produce a landslide susceptibility map of a landslide prone area using a weight of evidence model; Geomatics, Natural Hazards and Risk, Remote Sensing Science Center for Cultural Heritage, pp. 395–402.

  • Saha A K, Gupta R P, Sarkar I, Arora M K and Csaplovics E 2005 An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas; Landslides 2 61–69.

    Article  Google Scholar 

  • Sarkar S, Roy A K and Raha P 2016 Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India; Catena 142 36–46.

    Article  Google Scholar 

  • Shahabi H, Hashim M and Ahmad B B 2015 Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran; Environ. Earth Sci. 73 8647–8668.

    Article  Google Scholar 

  • Sharma S and Mahajan A K 2018 A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India; Bull. Eng. Geol. Environ., https://doi.org/10.1007/s10064-018-1259-9.

    Article  Google Scholar 

  • Sujatha E R, Kumaravel P and Rajamanickam G V 2014 Assessing landslide susceptibility using Bayesian probability-based weight of evidence model; Bull. Eng. Geol. Environ. 73(1) 147–161.

    Article  Google Scholar 

  • Thiart C, Bonham-Carter G F, Agterbreg F P, Cheng Q and Panahi A 2006 An application of the new omnibus test of conditional independence in weights-of-evidence modelling; In: GIS for the earth sciences (ed.) Harris J R, Vol. 44, Geological Association of Canada, Special Publication, pp. 131–142.

  • Umar Z, Pradhan B, Ahmad A, Jebur M N and Tehrany M S 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 

  • Valdiya K S 1980 Geology of Kumaun Lesser Himalaya Interim Report 291, Wadia Institute of Himalayan Geology, Dehradun.

  • Varnes D J 1984 International association of engineering geology commission on landslides and other mass movements on slopes: Landslide hazard zonation: A review of principles and practice; UNESCO, Paris, 63p.

    Google Scholar 

  • Watershed Management Directorate, Dehradun (WMDD) 2009 Report on Uttarakhand State perspective and strategic planning 2009–2027. http://wmduk.gov.in/Perspective_Plan_2009-2027.pdf.

  • Yan F, Zhang Q, Ye S and Ren B 2019 A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model; Geomorphology 327 170–187.

    Article  Google Scholar 

  • Yao X, Tham L G and Dai F C 2008 Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China; Geomorphology 101(4) 572–582.

    Article  Google Scholar 

  • Yilmaz I 2010 Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine; Environ. Earth Sci. 61 821–836.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge THDCL, Rishikesh, for giving ancillary help throughout this work. They offer their sincere gratitude to the Department of Earth Sciences, IIT Roorkee, for providing laboratory and software facility for carrying out this work. They also acknowledge Punjab Engineering College, Chandigarh, for allowing them to complete the paper.

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Correspondence to Rohan Kumar.

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Corresponding editor: Arkoprovo Biswas

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Kumar, R., Anbalagan, R. Landslide susceptibility mapping of the Tehri reservoir rim area using the weights of evidence method. J Earth Syst Sci 128, 153 (2019). https://doi.org/10.1007/s12040-019-1159-9

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  • DOI: https://doi.org/10.1007/s12040-019-1159-9

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