Bulletin of Engineering Geology and the Environment

, Volume 78, Issue 6, pp 4201–4215 | Cite as

Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods

  • Guoliang Du
  • Yongshuang ZhangEmail author
  • Zhihua Yang
  • Changbao Guo
  • Xin Yao
  • Dongyan Sun
Original Paper


The eastern Himalayan syntaxis in Tibet is one of the regions tectonically most active with the fastest uplift rate on the earth, where landslides are extremely frequent, causing severe damage to lives and transportation and inducing poverty. Thus, mapping landslide susceptibility of this area is of great importance. The purpose of this study is to compare landslide susceptibility maps for this region produced by the analytic hierarchy process information value (AHPIV) and logistic regression-information value (LRIV) methods using geographic information system (GIS) software. To do this, an inventory map with 799 landslides was prepared based on historical documents, interpretation of aerial photographs, and extensive field surveys. A total of eight conditioning factors were analyzed as input variables: lithology, slope gradient, slope aspect, elevation, curvature, distance to faults, distance to drainages and distance to roads. Then, the AHPIV and LRIV methods were applied to mapping landslide susceptibility. The performances of the methods were validated and compared using receiver operating characteristics (ROC) curves. The area under the curve (AUC) values obtained using the AHPIV and LRIV methods were 0.884, and 0.906, respectively. Results showed that the LRIV method performs better than the AHPIV method. Finally, sensitivity analyses were performed to examine the effects of removing any of the conditioning factors on the landslide susceptibility mapping. Results indicate that all of the conditioning factors have a positive effect on the landslide susceptibility mapping. Therefore, the LRIV method with eight conditioning factors was employed to determine potential landslide zones in the study area for landslide management and decision making.


Landslide susceptibility GIS Analytical hierarchy process information value Logistic regression-information value Eastern Himalayan syntaxis 



This study is supported by the National Natural Science Foundation of China (41731287, 41807231) and China Geological Survey Project (12120113038000).


  1. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44CrossRefGoogle Scholar
  2. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 25(3):559–572CrossRefGoogle Scholar
  3. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31CrossRefGoogle Scholar
  4. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata prefecture, Japan. Landslides 1(1):73–81CrossRefGoogle Scholar
  5. Binaghi E, Luzi L, Madella P et al (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches. Nat Hazards 17(1):77–97CrossRefGoogle Scholar
  6. Bui DT, Lofman O, Revhaug I et al (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59(3):1413–1444CrossRefGoogle Scholar
  7. Bui DT, Tuan TA, Klempe H et al (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378CrossRefGoogle Scholar
  8. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16(5):427–445CrossRefGoogle Scholar
  9. Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surface Proc Landform: J Bri Geomorphol Res Group 28(10):1125–1142CrossRefGoogle Scholar
  10. Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75(10):1–16Google Scholar
  11. Chen W, Xie X, Wang J, Pradhan B et al (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160CrossRefGoogle Scholar
  12. Conforti M, Aucelli PPC, Robustelli G et al (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (northern Calabria, Italy). Nat Hazards 56(3):881–898CrossRefGoogle Scholar
  13. Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43(1):27–29CrossRefGoogle Scholar
  14. Devkota KC, Regmi AD, Pourghasemi HR et al (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):135–165CrossRefGoogle Scholar
  15. Du GL, Zhang YS, Lv WM et al (2016) Landslide susceptibility assessment based on weighted information value model in Southeast Tibet. J Catastrophol 31(2):226–234 (In Chinese)Google Scholar
  16. Du GL, Zhang YS, Iqbal J et al (2017a) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14(2):249–268CrossRefGoogle Scholar
  17. Du GL, Zhang YS, Yang ZH et al (2017b) Estimation of seismic landslide Hazard in the eastern Himalayan Syntaxis region of Tibetan plateau. Acta Geol Sin (English Edition) 91(2):658–668CrossRefGoogle Scholar
  18. Ercanoglu M, Kasmer O, Temiz N (2008) Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bull Eng Geol Environ 67(4):565–578CrossRefGoogle Scholar
  19. Fan LF, Hu RL, Zeng FC et al (2012) Application of weighted information value model to landslide susceptibility assessment - a case study of Enshi city, Hubei province. J Eng Geol 20(4):508–513 (In Chinese)Google Scholar
  20. Feizizadeh B, Roodposhti M S, Jankowski P, et al (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221Google Scholar
  21. Fenta AA, Kifle A, Gebreyohannes T, Hailu G (2015) Spatial analysis of groundwater potential using remote sensing and GIS-based multicriteria evaluation in Raya Valley, northern Ethiopia. Hydrogeol J 23(1):195–206CrossRefGoogle Scholar
  22. Gallo F, Lavé J (2014) Evolution of a large landslide in the high Himalaya of Central Nepal during the last half-century. Geomorphology 223:20–32CrossRefGoogle Scholar
  23. Ghosh S, Carranza EJM, Van Westen CJ et al (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131(1):35–56CrossRefGoogle Scholar
  24. Godt JW, Baum RL, Savage WZ et al (2008) Transient deterministic shallow landslide modeling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol 102(3–4):214–226CrossRefGoogle Scholar
  25. Guo CB, Montgomery DR, Zhang YS et al (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan plateau, China. Geomorphology 248:93–110CrossRefGoogle Scholar
  26. Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179CrossRefGoogle Scholar
  27. Hong H, Liu J, Bui DT et al (2018) Landslide susceptibility mapping using J48 decision tree with AdaBoost, bagging and rotation Forest ensembles in the Guangchang area (China). Catena 163:399–413CrossRefGoogle Scholar
  28. Kayastha P, Dhital MR, De SF (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, West Nepal. Comput Geosci 52:398–408CrossRefGoogle Scholar
  29. Lan HX, Zhou CH, Wang LJ et al (2004) Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Eng Geol 76(1):109–128CrossRefGoogle Scholar
  30. Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672CrossRefGoogle Scholar
  31. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47(7):982–990CrossRefGoogle Scholar
  32. Lin ML, Tung CC (2004) A GIS-based potential analysis of the landslides induced by the chi-chi earthquake. Eng Geol 71(1):63–77CrossRefGoogle Scholar
  33. Liu GR, Yan EC, Lian C (2002) Discussion on classification of landslides. J Eng Geol 10(4):339–342 (in Chinese)Google Scholar
  34. Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in Central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97CrossRefGoogle Scholar
  35. Meng H, Zhang YQ, Yang N (2004) Analysis of the spatial distribution of geohazards along the middle segment of the eastern margin of the Qinghai-Tibet plateau. Geol China 31(2):218–224 (In Chinese)Google Scholar
  36. Mezughi TH, Akhir JM, Rafek AGM et al (2011) Landslide susceptibility assessment using frequency ratio model applied to an area along the EW highway (Gerik-Jeli). Am J Environ Sci 7(1):43–50CrossRefGoogle Scholar
  37. Mondal S, Maiti R (2013) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of shiv-khola watershed, Darjeeling Himalaya. Int J Disas Risk Sci 4(4):200–212CrossRefGoogle Scholar
  38. Park NW (2010) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62(2):367–376CrossRefGoogle Scholar
  39. Park HJ, Lee JH, Woo I (2013) Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach. Eng Geol 161:1–15CrossRefGoogle Scholar
  40. Pourghasemi HR, Moradi HR, Aghda SMF (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779CrossRefGoogle Scholar
  41. 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):747–759CrossRefGoogle Scholar
  42. Regmi AD, Yoshida K, Pourghasemi HR et al (2014) Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285CrossRefGoogle Scholar
  43. Sarkar S, Kanungo DP, Patra AK et al (2008) GIS based spatial data analysis for landslide susceptibility mapping. J Mt Sci 5(1):52–62CrossRefGoogle Scholar
  44. Shang YJ, Park HD, Yang ZF et al (2005) Distribution of landslides adjacent to the northern side of the Yarlu Tsangpo grand canyon in Tibet, China. Environ Geol 48(6):721–741CrossRefGoogle Scholar
  45. Sharma M, Kumar R (2008) GIS-based landslide hazard zonation: a case study from the Parwanoo area, lesser and outer Himalaya, H.P., India. Bull Eng Geol Environ 67(1):129–137CrossRefGoogle Scholar
  46. Suh J, Choi Y, Roh TD et al (2011) National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environ Earth Sci 63(3):619–632CrossRefGoogle Scholar
  47. Tahmassebipoor N, Rahmati O, Noormohamadi F et al (2016) Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9(1):1–18CrossRefGoogle Scholar
  48. Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179CrossRefGoogle Scholar
  49. Umar Z, Pradhan B, Ahmad A et al (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in west Sumatera Province, Indonesia. Catena 118:124–135CrossRefGoogle Scholar
  50. Wang F, Xu P, Wang C et al (2017) Application of a GIS-based slope unit method for landslide susceptibility mapping along the Longzi River, southeastern Tibetan plateau, China. ISPRS Int J Geo-Inform 6(6):172CrossRefGoogle Scholar
  51. Yang ZH, Lan HX, Gao X et al (2015) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75(3):2467–2487CrossRefGoogle Scholar
  52. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582CrossRefGoogle Scholar
  53. Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68(3):297–306CrossRefGoogle Scholar
  54. 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(4):821–836CrossRefGoogle Scholar
  55. Yilmaz I, Keskin I (2009) GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68(4):459–471CrossRefGoogle Scholar
  56. Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks[C]//proceedings of the 5th international symposium on landslides, Lausanne. Switzerland 2:1269–1272Google Scholar
  57. Yin KL, Yan TZ (1996) Landslide prediction and relevant models. Chin J Rock Mech Eng 15(1):1–8 (In Chinese)Google Scholar
  58. Youssef AM, Pradhan B, Jebur MN et al (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73(7):3745–3761CrossRefGoogle Scholar
  59. Zhou SH, Wang W, Chen GQ et al (2016) A combined weight of evidence and logistic regression method for susceptibility mapping of earthquake-induced landslides: a case study of the April 20, 2013 Lushan earthquake, China. Acta Geol Sin (English Edition) 90(2):511–524CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Guoliang Du
    • 1
  • Yongshuang Zhang
    • 2
    Email author
  • Zhihua Yang
    • 3
  • Changbao Guo
    • 3
  • Xin Yao
    • 3
  • Dongyan Sun
    • 1
  1. 1.Hebei GEO UniversityShijiazhuangChina
  2. 2.Tianjin Center, China Geological SurveyTianjinChina
  3. 3.Key Laboratory of Neotectonic Movement and Geohazard, Institute of GeomechanicsChinese Academy of Geological SciencesBeijingChina

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