Natural Hazards

, Volume 79, Issue 1, pp 511–536 | Cite as

Prediction of highway blockage caused by earthquake-induced landslides for improving earthquake emergency response

  • Jiwen An
  • Xianfu Bai
  • Jinghai Xu
  • Gaozhong Nie
  • Xiuying Wang
Original Paper


Earthquake emergency response (EER) supported by the prompt assessment of seismic impact is an effective way to reduce seismic casualties and losses after an earthquake. However, in mountainous areas, highway blockages due to earthquake-induced landslides can delay EER, which, to date, EER planning has not included in assessments to identify. This paper proposes a set of rules to predict the location of highway blockages caused by these landslides. Such predictions would promote rapid implementation of traffic control plans and the prompt clearing of the blocked highways to help keep emergency efforts efficient. We propose a procedure based on the decision tree method to correlate the potential highway blockages with the earthquake-induced landslide susceptibility (ELS), which integrates the classification and quantification aspects of the ELS. Using correlation analysis, a set of rules that judge whether a highway section is likely to be blocked is proposed. These rules are based on the preexisting ELS database for China. This set of rules has been applied in a case study of the 2014 Ludian earthquake to predict the highway blockages caused by the earthquake-induced landslides. The results from this case study showed good agreement with the actual highway blockages as determined by the interpretation of unmanned aerial vehicle images. The predicted results were used to make suggestions about traffic control and blocked highway clearing for EER. The proposed set of rules appears to be effective.


Prediction Highway blockage Earthquake-induced landslide susceptibility Earthquake emergency response Correlation analysis Decision tree 



This work was jointly supported by the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University) (Grant No. 201404), the Special Fund for Basic Scientific Research Operations in Institute of Geology, CEA (Grant No. IGCEA1109), the Jiangsu Surveying, Mapping and Geoinformation Science Research Project (Grant No. JSCHKY201506), and the National Key Technology R&D Program (Grant No. 2012BAK15B06). We sincerely thank T.S. Murty and the anonymous reviewers for their comments and suggestions that greatly improved the manuscript.


  1. Alkhasawneh MS, Ngah UK, Tay LT, Mat Isa NA, Al-Batah MS (2014) Modeling and testing landslide hazard using decision tree. J Appl Math 2014:9. doi: 10.1155/2014/929768 CrossRefGoogle Scholar
  2. Bai S-b, Cheng C, Wang J, Thiebes B, Zhang Z-g (2013) Regional scale rainfall-and earthquake-triggered landslide susceptibility assessment in Wudu County, China. J Mt Sci 10:743–753. doi: 10.1007/s11629-013-2432-z
  3. Bai X, Dai Y, Yu Q, Shao W (2015) Risk assessment modeling of earthquake-induced landslides and its preliminary application. J Seismol Res 38:301–312 (in Chinese) Google Scholar
  4. Bozzano F et al (2013) Earthquake-reactivated landslide scenarios in Southern Italy based on spectral-matching input analysis. Bull Earthq Eng 11:1927–1948. doi: 10.1007/s10518-013-9477-9 CrossRefGoogle Scholar
  5. Capolongo D, Refice A, Mankelow J (2002) Evaluating earthquake-triggered landslide hazard at the basin scale through Gis in the Upper Sele river valley. Surv Geophys 23:595–625. doi: 10.1023/A:1021235029496 CrossRefGoogle Scholar
  6. Chen XL, Yu L, Wang MM, Lin CX, Liu CG, Li JY (2014) Brief communication: landslides triggered by the Ms = 7.0 Lushan earthquake, China. Nat Hazards Earth Syst Sci 14:1257–1267. doi: 10.5194/nhess-14-1257-2014 CrossRefGoogle Scholar
  7. Chousianitis K, Del Gaudio V, Kalogeras I, Ganas A (2014) Predictive model of arias intensity and newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece. Soil Dyn Earthq Eng 65:11–29. doi: 10.1016/j.soildyn.2014.05.009 CrossRefGoogle Scholar
  8. Cui X, Miao Q, Wang J (2010) Model of the seismic intensity attenuation for North China. North China Earthq Sci 28:18–21 (in Chinese) Google Scholar
  9. Dai FC, Xu C, Yao X, Xu L, Tu XB, Gong QM (2011) Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake. China J Asian Earth Sci 40:883–895. doi: 10.1016/j.jseaes.2010.04.010 CrossRefGoogle Scholar
  10. Del Gaudio V, Wasowski J (2004) Time probabilistic evaluation of seismically induced landslide hazard in Irpinia (Southern Italy). Soil Dyn Earthq Eng 24:915–928. doi: 10.1016/j.soildyn.2004.06.019 CrossRefGoogle Scholar
  11. Del Gaudio V, Pierri P, Calcagnile G (2012) Analysis of seismic hazard in landslide-prone regions: criteria and example for an area of Daunia (southern Italy). Nat Hazards 61:203–215. doi: 10.1007/s11069-011-9886-5 CrossRefGoogle Scholar
  12. Faris F, Fawu W (2014) Investigation of the initiation mechanism of an earthquake- induced landslide during rainfall: a case study of the Tandikat landslide, West Sumatra, Indonesia. Geoenviron Disasters 1:1–18. doi: 10.1186/s40677-014-0004-3 CrossRefGoogle Scholar
  13. Garcia D, Mah RT, Johnson KL, Hearne MG, Marano KD, Lin KW, Wald DJ, Worden CB, So E (2012) ShakeMap Atlas 2.0: an improved suite of recent historical earthquake ShakeMaps for global hazard analyses and loss models, Proc. 15th World Conf. on Eq. Eng., Lisbon, 10pGoogle Scholar
  14. Gong M, Lin S, Sun J, Li S, Dai J, Xie L (2015) Seismic intensity map and typical structural damage of 2010 Ms 7.1 Yushu earthquake in China. Nat Hazards 77(2):847–866. doi: 10.1007/s11069-015-1631-z CrossRefGoogle Scholar
  15. Han Y, Cui P, Zhu Y, Su F, Zhang Y, Yang Y (2009) Remote sensing monitoring and assessment of traffical damage by Wenchuan Earthquake: a case study in Du-Wen highway, Sichuan Province, China. J Sichuan Univ (Engineering Science Edition) 41:273–283. (in Chinese) Google Scholar
  16. Havenith H-B, Strom A, Caceres F, Pirard E (2006) Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and geotechnical approach. Landslides 3:39–50. doi: 10.1007/s10346-005-0005-0 CrossRefGoogle Scholar
  17. He C-R, Chen Q, Han S-L, Zhang R (2011a) Earthquake characteristics and building damage in high-intensity areas of Wenchuan earthquake II: Dujiangyan and Pengzhou City. Nat Hazards 57:279–292. doi: 10.1007/s11069-010-9612-8 CrossRefGoogle Scholar
  18. He C-R, Zhang R, Chen Q, Han S-L (2011b) Earthquake characteristics and building damage in high-intensity areas of Wenchuan earthquake I: yingxiu town. Nat Hazards 57:435–451. doi: 10.1007/s11069-010-9624-4 CrossRefGoogle Scholar
  19. James N, Sitharam TG (2014) Assessment of seismically induced landslide hazard for the State of Karnataka using GIS technique. J Indian Soc Remote Sens 42:73–89. doi: 10.1007/s12524-013-0306-z CrossRefGoogle Scholar
  20. Jibson RW (2007) Regression models for estimating coseismic landslide displacement. Eng Geol 91:209–218. doi: 10.1016/j.enggeo.2007.01.013 CrossRefGoogle Scholar
  21. Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101:631–642. doi: 10.1016/j.geomorph.2008.03.003 CrossRefGoogle Scholar
  22. Kamp U, Owen L, Growley B, Khattak G (2010) Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps. Nat Hazards 54:1–25. doi: 10.1007/s11069-009-9451-7 CrossRefGoogle Scholar
  23. Lei J, Gao M, Yu Y (2007) Seismic motion attenuation relations in sichuan and adjacent areas. Acta Seismol Sin 29:500–511 (in Chinese) Google Scholar
  24. Lv J, Yu Y, Gao J, Gao D, Tang L (2009) Attenuation relation of seismic intensity in Jiangxi Province and its adjacent area. J Seismol Res 32:269–274 (in Chinese) Google Scholar
  25. Ma Z, Du P, Gao X, Qi W, Li X (2010) Analysis of earthquake distributions in East Asia and in the world. Earth Sci Front 17:215–233 (in Chinese) Google Scholar
  26. 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. doi: 10.1155/2010/901095 Google Scholar
  27. Newmark NM (1965) Effects of earthquakes on dams and embankments. Civil Eng Class 15:631–652Google Scholar
  28. 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 173:54–65. doi: 10.1016/j.enggeo.2014.02.002 CrossRefGoogle Scholar
  29. Ouyang Y (2013) Earthquake tests China’s emergency system. Lancet 381:1801–1802CrossRefGoogle Scholar
  30. 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. doi: 10.1016/j.cageo.2012.08.023 CrossRefGoogle Scholar
  31. Qin X-W et al (2009) Remote sensing emergency survey in Wenchuan earthquake area. The Science Press, Beijing, China (in Chinese)Google Scholar
  32. Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 109:108–121. doi: 10.1016/j.geomorph.2009.02.026 CrossRefGoogle Scholar
  33. Tang C, Zhu J, Qi X, Ding J (2011) Landslides induced by the Wenchuan earthquake and the subsequent strong rainfall event: a case study in the Beichuan area of China. Eng Geol 122:22–33. doi: 10.1016/j.enggeo.2011.03.013 CrossRefGoogle Scholar
  34. 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. doi: 10.1016/j.catena.2014.02.005 CrossRefGoogle Scholar
  35. Wang G (2014) Comparison of the landslides triggered by the 2013 Lushan earthquake with those triggered by the strong 2008 Wenchuan earthquake in areas with high seismic intensities. Bull Eng Geol Environ 74(1):77–89. doi: 10.1007/s10064-014-0574-z CrossRefGoogle Scholar
  36. Wang X, Niu R (2009) Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining. Sensors 9:2035–2061CrossRefGoogle Scholar
  37. Wegscheider S, Schneiderhan T, Mager A, Zwenzner H, Post J, Strunz G (2013) Rapid mapping in support of emergency response after earthquake events. Nat Hazards 68:181–195. doi: 10.1007/s11069-013-0589-y CrossRefGoogle Scholar
  38. Wei B, Su G, Liu F (2013) Public response to earthquake disaster: a case study in Yushu Tibetan Autonomous Prefecture. Nat Hazards 69:441–458. doi: 10.1007/s11069-013-0710-2 CrossRefGoogle Scholar
  39. Wiils CJ, Perez FG, Gutierrez CI (2011) “Susceptibility to deep-seated landslides in California” California geological survey map sheet 58. California Geol Surv, SacramentoGoogle Scholar
  40. Wu S, Jin J, Pan T (2015) Empirical seismic vulnerability curve for mortality: case study of China. Nat Hazards 1–18. doi: 10.1007/s11069-015-1613-1
  41. Xiao L, Yu Y (2011) Earthquake intensity attenuation relationship in Western China. Technol Earthq Disaster Prev 6:358–371 (in Chinese) Google Scholar
  42. Xu C, Dai F, Xu X, Lee YH (2012a) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80. doi: 10.1016/j.geomorph.2011.12.040 CrossRefGoogle Scholar
  43. Xu C, Xu X, Dai F, Xiao J, Tan X, Yuan R (2012b) Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. J Earth Sci 23:97–120. doi: 10.1007/s12583-012-0236-7 CrossRefGoogle Scholar
  44. Xu C et al (2013a) 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 68:883–900. doi: 10.1007/s11069-013-0661-7 CrossRefGoogle Scholar
  45. Xu C, Xu X, Yao X, Dai F (2013b) 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 11:441–461. doi: 10.1007/s10346-013-0404-6 CrossRefGoogle Scholar
  46. Xu C, Xu X, Zhou B, Yu G (2013c) Revisions of the M 8.0 Wenchuan earthquake seismic intensity map based on co-seismic landslide abundance. Nat Hazards 69:1459–1476. doi: 10.1007/s11069-013-0757-0 CrossRefGoogle Scholar
  47. Xu J, Nyerges TL, Nie G (2014) Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology. Int J Geogr Inf Sci 28:185–205. doi: 10.1080/13658816.2013.845893 CrossRefGoogle Scholar
  48. Yang Z-h, Lan H-x, Gao X, Li L-p, Meng Y-s, Wu Y-m (2015) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75:2467–2487. doi: 10.1007/s11069-014-1441-8 CrossRefGoogle Scholar
  49. Zhou Z, He S, Chen W (2010) Study on the attenuation relationship of seismic intensity in Gansu Province. Northwest Seismol J 32:72–75 (in Chinese) Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jiwen An
    • 1
  • Xianfu Bai
    • 2
  • Jinghai Xu
    • 3
  • Gaozhong Nie
    • 1
  • Xiuying Wang
    • 4
  1. 1.Institute of GeologyChina Earthquake AdministrationBeijingChina
  2. 2.Yunnan Earthquake AdministrationKunmingChina
  3. 3.College of Geomatics EngineeringNanjing Tech UniversityNanjingChina
  4. 4.Institute of Crustal DynamicsChina Earthquake AdministrationBeijingChina

Personalised recommendations