Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: a case study of the 2013 Lushan, China, Mw6.6 earthquake

  • Siyuan Ma
  • Chong XuEmail author
Original Paper


The April 20, 2013 Mw6.6 earthquake of Lushan County, Sichuan Province, China, has triggered 4540 landslides (> 1000 m2). Exploring a more effective method to assess landslide hazard in the affected area of this event is of great significance for disaster prevention and mitigation. By applying the Newmark model and two statistical analysis models (logic regression and support vector machine, LR and SVM), this study addressed this issue. In the Newmark model, we used the landslide point density, the average gradient (mean slope) and the mean peak ground acceleration to group the lithology and created a critical acceleration (ac) map. The Newmark displacements and the probability of the slope instability are mapped by combining the ac map and PGA map. In the statistical analysis models of LR and SVM, 7040 samples (4540 landslide sites and 2500 random non-landslide sites) were randomly divided into the training set (5000 samples) and validation set (2040 samples). Based on the relationship between landslide distribution and influence factors, we selected the critical acceleration (ac) value, topographic relief, PGA, and distance to rivers as the independent variables for LR and SVM. Finally, the ROC curves for three landslide hazard models were drawn and the AUC values were calculated. The landslide hazard maps produced by LR are similar to those by applying SVM. The AUC values indicate that these two models combined with ac data perform better than the simplified Newmark model. In this study, a new method of integrating statistical analysis models (LR and SVM) with critical acceleration (ac) for earthquake landslide hazard assessment is presented, which can be used to carry out seismic landslide hazard assessment more effectively than the simplified Newmark model.


Lushan earthquake Co-seismic landslides Hazard Newmark model Statistical analysis 



This study was supported by the National Natural Science Foundation of China (41472202).


  1. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106CrossRefGoogle Scholar
  2. Al-Homoud AS, Tahtamoni W (2000) Comparison between predictions using different simplified Newmarks’ block-on-plane models and field values of earthquake induced displacements. Soil Dyn Earthq Eng 19:73–90CrossRefGoogle Scholar
  3. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36CrossRefGoogle Scholar
  4. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31CrossRefGoogle Scholar
  5. Bai SB, Ping LU, Jian W (2015) Landslide susceptibility assessment of the Youfang Catchment using logistic regression. J Moutain Sci 12:816–827CrossRefGoogle Scholar
  6. Bray JD, Travasarou T (2007) Simplified procedure for estimating earthquake-induced deviatoric slope displacements. J Geotech Geoenviron Eng 133:381–392CrossRefGoogle Scholar
  7. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862CrossRefGoogle Scholar
  8. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27CrossRefGoogle Scholar
  9. Chen XL, Yuan RM, Yu L (2014a) Applying the Newmark’s model of the assessment of earthquake-triggered landslides during the Lushan earthquake. Seismol Geol 35:661–670 (in Chinese) Google Scholar
  10. Chen XL, Liu CG, Yu L, Lin C (2014b) Critical acceleration as a criterion in seismic landslide susceptibility assessment. Geomorphology 217:15–22CrossRefGoogle Scholar
  11. Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391CrossRefGoogle Scholar
  12. Deng QD, Ran YK, Yang XP, Min W, Chu QZ (2007) Map of active fault in China. Seismological Press, Beijing (in Chinese) Google Scholar
  13. Dreyfus DK (2011) A comparison of methodologies used to predict earthquake-induced landslides. PhD University of TexasGoogle Scholar
  14. Dreyfus DK, Rathje EM, Jibson RW (2013) The influence of different simplified sliding-block models and input parameters on regional predictions of seismic landslides triggered by the Northridge earthquake. Eng Geol 163:41–54CrossRefGoogle Scholar
  15. Gallen SF, Clark MK, Godt JW (2015) Coseismic landslides reveal near-surface rock strength in a high-relief, tectonically active setting. Geology 43:11–14CrossRefGoogle Scholar
  16. Gallen SF, Clark MK, Godt JW, Roback K, Niemi NA (2016) Application and evaluation of a rapid response earthquake-triggered landslide model to the 25 April 2015 Mw 7.8 Gorkha earthquake, Nepal. TectonophysicsGoogle Scholar
  17. Godt JW, Sener B, Verdin KL, Wald DJ, Earle PS, Harp EL, Jibson RW (2008). Rapid assessment of earthquake-induced landsliding. In: Tokyo, Japan: proceedings of the first world landslide forumGoogle Scholar
  18. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216CrossRefGoogle Scholar
  19. Hong H, Pradhan B, Xu C, Tien Bui D (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 133:266–281CrossRefGoogle Scholar
  20. Hosmer DW, Lemeshow S (2005) Multiple logistic regression, in applied logistic regression, 2nd edn. Wiley, New York, pp 31–46CrossRefGoogle Scholar
  21. Hu DY, Li J, Hao CY, Shui ZJ (2007) GIS-based landslide spatial prediction methods, a case study in Cameron Highland, Malaysia. J Remote Sens 11:852–859Google Scholar
  22. Huang J, Zhou Q, Wang F (2015) Mapping the landslide susceptibility in Lantau Island, Hong Kong, by frequency ratio and logistic regression model. Geograph Inf Sci 21:191–208Google Scholar
  23. Jian SC, Yong L, Kun YZ, Zhou N, Long ZL, Liang Y, Bo LJ (2007) Research on the DEM of topographic relief in Longmenshan river basin. J Sichuan Norm Univ 38:766–773Google Scholar
  24. Jibson RW (1993) Predicting earthquake-induced landslide displacements using Newmark’s sliding block analysis. Transportation Research RecordGoogle Scholar
  25. Jibson RW (2007) Regression models for estimating coseismic landslide displacement. Eng Geol 91:209–218CrossRefGoogle Scholar
  26. Jibson RW, Michael JA (2009) Maps showing seismic landslide hazards in Anchorage. Center for Integrated Data Analytics Wisconsin Science Center, AlaskaGoogle Scholar
  27. Jibson RW, Harp EL, Michael JA (1998) A method for producing digital probabilistic seismic landslide hazard maps: an example from the Los Angeles, California, area. Open-File ReportGoogle Scholar
  28. Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps: an example from the Los Angeles, California, area. Eng Geol 58:271–289CrossRefGoogle Scholar
  29. Kavzoglu T, Sahin EK, Colkesen I (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Hazards 76:471–496CrossRefGoogle Scholar
  30. Kaynia AM, Skurtveit E, Saygili G (2011) Real-time mapping of earthquake-induced landslides. Bull Earthq Eng 9:955–973CrossRefGoogle Scholar
  31. Keefer DK (1984) Landslides caused by earthquakes. Geol Soc Am Bull 95:406CrossRefGoogle Scholar
  32. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491CrossRefGoogle Scholar
  33. Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234CrossRefGoogle Scholar
  34. McCrink TP (2001) Regional earthquake-induced landslide mapping using Newmark displacement criteria. San Cruz County, California, pp 77–92Google Scholar
  35. Miles SB, Ho CL (1999) Rigorous landslide hazard zonation using Newmark’s method and stochastic ground motion simulation. Soil Dyn Earthq Eng 18:305–323CrossRefGoogle Scholar
  36. Ministry of Construction of the People’s Republic of China (2009) Code for geotechnical engineering investigation GB 50021-2001 (2009). National Bureau of Quality Inspection (in Chinese)Google Scholar
  37. Ministry of Water Resources of the People’s Republic of China (2014) Standard for engineering classification of rock masses GB/T 50218-2014. Standards Press of China, Beijing (in Chinese) Google Scholar
  38. Newmark NM (1965) Effects of earthquakes on dams and embankments. Géotechnique 15:139–160CrossRefGoogle Scholar
  39. Nowicki Jessee MA, Hamburger MW, Allstadt K, Wald DJ, Robeson SM, Tanyas H, Hearne M, Thompson EM (2018) A global empirical model for near-real-time assessment of seismically induced landslides. J Geophys Res Earth Surf 123:1835–1859Google Scholar
  40. Nowicki MA, Wald DJ, Hamburger MW, Hearne M, Thompson EM (2014) Development of a globally applicable model for near real-time prediction of seismically induced landslides. Eng Geol 173:54–65CrossRefGoogle Scholar
  41. Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343CrossRefGoogle Scholar
  42. 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 51:350–365CrossRefGoogle Scholar
  43. Pradel D, Smith PM, Stewart JP, Raad G (2005) Case history of landslide movement during the Northridge earthquake. J Geotech Geoenviron Eng 131:1360–1369CrossRefGoogle Scholar
  44. Rao G, Cheng YL, Lin AM, Yan B (2017) Relationship between landslides and active normal faulting in the epicentral area of the AD 1556 M ~ 8.5 Huaxian Earthquake, SE Weihe Graben (Central China). J Earth Sci 28:545–554CrossRefGoogle Scholar
  45. Rathje EM (2008) Probabilistic seismic hazard analysis for the sliding displacement of slopes. J Geotech Geoenviron Eng 134:804–814CrossRefGoogle Scholar
  46. San BT (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: the Candir catchment area (western Antalya, Turkey). Int J Appl Earth Obs Geoinf 26:399–412CrossRefGoogle Scholar
  47. Saygili G, Rathje EM (2008) Empirical predictive models for earthquake-induced sliding displacements of slopes. J Geotech Geoenviron Eng 134:790–803CrossRefGoogle Scholar
  48. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293CrossRefGoogle Scholar
  49. 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 118:124–135CrossRefGoogle Scholar
  50. Wilson RC, Keefer DK (1983) Dynamic analysis of a slope failure from the 6 August 1979 Coyote Lake, California, Earthquake. Bull Deismolog Soc Am 73:863–877Google Scholar
  51. Xu C, Xu XW (2012) The 2010 Yushu earthquake triggered landslides spatial prediction models based on several kernel function types. Chin J Geophys 55:2994–3005 (in Chinese) Google Scholar
  52. Xu XW, Wen XZ, Yu G, Chen G, Klinger Y, Hubbard J, Shaw J (2009) Coseismic reverse- and oblique-slip surface faulting generated by the 2008 Mw 7.9 Wenchuan earthquake, China. Geology 37:515–518CrossRefGoogle Scholar
  53. Xu C, Xu X, Dai F, Saraf AK (2012a) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput Geosci 46:317–329CrossRefGoogle Scholar
  54. Xu C, Dai F, Xu X, Yuan HL (2012b) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80CrossRefGoogle Scholar
  55. Xu C, Xu XW, Yao Q, Wang Y (2013a) GIS-based bivariate statistical modelling for earthquake triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China. Q J Eng Geol Hydrogeol 46:221–236CrossRefGoogle Scholar
  56. Xu C, Xu XW, Dai FC, Wu Z, He H, Shi F, Wu X, Xu S (2013b) Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Nat Hazards 68:883–900CrossRefGoogle Scholar
  57. Xu XW, Wen XZ, Han ZJ (2013c) Lushan Ms 7.0 earthquake: a blind reserve-fault earthquake. Chin Sci Bull 58:1887–1893CrossRefGoogle Scholar
  58. Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11:441–461CrossRefGoogle Scholar
  59. Xu C, Xu X, Shyu JBH (2015) Database and spatial distribution of landslides triggered by the Lushan, China Mw6.6 earthquake of 20 April 2013. Geomorphology 248:77–92CrossRefGoogle Scholar
  60. Xu C, Xu X, Tian Y, Shen L, Yao Q, Huang X, Ma J, Chen X, Ma S (2016) Two comparable earthquakes produced greatly different coseismic landslides: the 2015 Gorkha, Nepal and 2008 Wenchuan, China events. J Earth Sci 27:1008–1015CrossRefGoogle Scholar
  61. Xu C, Ma S, Tan Z, Xie C, Toda S, Huang X (2018) Landslides triggered by the 2016 Mj 7.3 Kumamoto, Japan, earthquake. Landslides 15:551–564CrossRefGoogle Scholar
  62. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA 72(1):1–12CrossRefGoogle Scholar
  63. 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 101:572–582CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Key Laboratory of Active Tectonics and Volcano, Institute of GeologyChina Earthquake AdministrationBeijingChina

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