Predicting the probability of rockfalls occurrence caused by the earthquake of Changureh-Avaj in 2002 using LR, MLP, and RBF methods

  • V. Bagheri
  • A. UromeihyEmail author
  • S. M. Fatemi Aghda
Original Paper


Rockfalls pose serious threats to human beings, structures, and lifelines. In the present research, a model was developed to predict the possibility of seismic rockfalls on a regional scale. For this purpose, three models including logistic regression (LR), a multilayer perceptron artificial neural network (MLP), and a radial basis function artificial neural network (RBF) were employed. Bivariate logistic regression is a multivariable statistical method that provides a mathematical model using independent variables to predict the occurrence probability of a given phenomenon at a certain location. Although artificial networks of RBF and MLP are pretty similar, there are some structural differences in the components between these two neural networks. The earthquake of Changureh-Avaj in 2002 was used as a basis and benchmark for the model presented in this work. The sustainable zones predicted by LR, MLP, and RBF methods were compared with a database (distribution map) of seismic rockfalls. The results showed good overlap between RBF-predicted rockfall susceptibility zones and database (distribution map) of seismic rockfalls. Besides, in order to evaluate the statistical results of LR, MLP, and RBF models, the verification parameters with high accuracy such as density ratio (Dr), quality sum (Qs), and Receiver Operating Characteristic Curve (ROC) were used. By analyzing the susceptibility maps and considering the Qs index obtained by LR (2.94) and MLP (3.482), and RBF (4.344), it could be observed that the Qs of RBF were higher than that of LR and MLP. Moreover, based on the obtained value of AUC from LR (0.859), MLP (0.910), and RBF methods (0.956), it is seen that the RBF method provided a higher accuracy in predicting the probability of rockfalls occurrence caused by the earthquake of Changureh-Avaj in 2002 compared to LR and MLP methods.


Seismic Rockfalls Hazard zonation Changureh-Avaj Logistic regression Artificial neural network 


  1. Agliardi F, Crosta GB, Frattini P (2009) Integrating rockfall risk assessment and countermeasure design by 3D modelling techniques. Nat Hazards Earth Syst Sci 9(4):1059Google Scholar
  2. 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):15–31Google Scholar
  3. Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Physics and Chemistry of the Earth, Parts A/B/C 35(3):162–171Google Scholar
  4. Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12(3):419–436Google Scholar
  5. Cancelli A, Crosta G (1993) Rockfall hazard and risk mapping. In: Proceedings 7th International Conference and Field Workshop on Landslides, Czech-Slovak Rep., Balkema, 69–76Google Scholar
  6. Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45(1):55–72Google Scholar
  7. 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(6):595–625Google Scholar
  8. Castelli F, Cavallaro A, Grasso S, Lentini V (2016) Seismic microzoning from synthetic ground motion earthquake scenarios parameters: the case study of the City of Catania (Italy). Soil Dyn Earthq Eng 88:307–327Google Scholar
  9. Chau KT, Wong RHC, Liu J, Lee CF (2003) Rockfall hazard analysis for Hong Kong based on rockfall inventory. Rock Mech Rock Eng 36(5):383–408Google Scholar
  10. Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411–423Google Scholar
  11. Clague JJ, Stead D (Eds.) (2012). Landslides: types, mechanisms and modeling. Cambridge University Press, 436 ppGoogle Scholar
  12. Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental Earth Sciences 63(2):397–406Google Scholar
  13. Corominas J, Copons R, Moya J, Vilaplana JM, Altimir J, Amigó J (2005) Quantitative assessment of the residual risk in a rockfall protected area. Landslides 2(4):343–357Google Scholar
  14. Corominas J, Mavrouli O, Ruiz-Carulla R (2017) Rockfall occurrence and fragmentation. In: Workshop on World Landslide Forum. Springer, 75–97Google Scholar
  15. Cox DR (1958) The regression analysis of binary sequences. J R Stat Soc Ser B Methodol 20(2):215–242Google Scholar
  16. Crosta GB, Agliardi F (2003) Failure forecast for large rock slides by surface displacement measurements. Can Geotech J 40(1):176–191Google Scholar
  17. Cruden DM, Varnes DJ (1996) Landslide types and processes. In Landslides: Investigation and Mitigation, ed. A. K. Turner and R. L. Schuster. Transportation Research Board, Special Report 247, 36–75Google Scholar
  18. Dashti Marvily M (2008) Landslide hazard zoning using logistic regression (Case Study:A Part of Gamasiab Watershed). M.Sc Thesis. Tarbiat Modares University, [in Persian], 92 ppGoogle Scholar
  19. Del Gaudio V, Wasowski J (2004) Time probabilistic evaluation of seismically induced landslide hazard in Irpinia (southern Italy). Soil Dyn Earthq Eng 24(12):915–928Google Scholar
  20. Del Gaudio V, Pierri P, Wasowski J (2003) An approach to time-probabilistic evaluation of seismically induced landslide hazard. Bull Seismol Soc Am 93(2):557–569Google Scholar
  21. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730Google Scholar
  22. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1):327–343Google Scholar
  23. Fatemi Aghda SM, Sarikhani R, Teshneh Lab M (2003) Landslide zonation in Talesh area using the expert systems (MLP network). Journal of Engineering Geology, [in Persian] 1(2):179–192Google Scholar
  24. Garsia-Rodríguez MJ, Malpica JA, Benito B, Díaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3):172–191Google Scholar
  25. Gee MD (1992) Classification of landslide hazard zonation methods and a test of predictive capability. Proceedings of 6th international symposium on landslides, Christchurch. New Zealand 2:947–952Google Scholar
  26. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River basin, Venezuela. Eng Geol 78(1):11–27Google Scholar
  27. Gosar A (2017) Earthquake-Induced rockfalls caused by 1998 Mw5. 6 Earthquake in Krn Mountains (NW Slovenia) and ESI 2007 Intensity Scale. In: Workshop on World Landslide Forum. Springer, 131–139Google Scholar
  28. Hagan MT, Demuth HB, Beale MH, Jesús OD (2014) Neural network design. Martin Hagan (2 edition), 1012 ppGoogle Scholar
  29. Hungr O, Evans SG, Hazzard J (1999) Magnitude and frequency of rock falls and rock slides along the main transportation corridors of southwestern British Columbia. Can Geotech J 36(2):224–238Google Scholar
  30. Jaboyedoff M, Dudt JP, Labiouse V (2005) An attempt to refine rockfall hazard zoning based on the kinetic energy, frequency and fragmentation degree. Natural Hazards and Earth System Science 5(5):621–632Google Scholar
  31. Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58(3):271–289Google Scholar
  32. Keefer DK (1984) Landslides caused by earthquakes. Geol Soc Am Bull 95(4):406–421Google Scholar
  33. Lari S, Frattini P, Crosta GB (2014) A probabilistic approach for landslide hazard analysis. Eng Geol 182:3–14Google Scholar
  34. Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855Google Scholar
  35. Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3):289–302Google Scholar
  36. Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–220Google Scholar
  37. Lee CT, Huang CC, Lee JF, Pan KL, Lin ML, Dong JJ (2008) Statistical approach to earthquake-induced landslide susceptibility. Eng Geol 100(1):43–58Google Scholar
  38. Mahdavifar MR, Solaymani S, Jafari MK (2006) Landslides triggered by the Avaj, Iran earthquake of June 22, 2002. Eng Geol 86(2):166–182Google Scholar
  39. Mahdavifar M, Askari F, Memarian P, Seyedimorad SM (2016) Earthquake-induced rock fall hazard zonation of Varzegha-Ahar region in Northwest Iran: a comparison of quantitative and qualitative approaches. Journal of Seismology and Earthquake Engineering 18(2):101–116Google Scholar
  40. Marzorati S, Luzi L, De Amicis M (2002) Rock falls induced by earthquakes: a statistical approach. Soil Dyn Earthq Eng 22(7):565–577Google Scholar
  41. Massey CI, MacSaveney MJ, Richards L (2015) Characteristics of some Rockfalls triggered by the 2010/2011 Canterbury earthquake sequence, New Zealand. Engineering Geology for Society and Territorym, Springer 2:1943–1948Google Scholar
  42. Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6(1):17–26Google Scholar
  43. Menhaj MB (2003) Basics of artificial networks: computation intelligence. Amir Kabir University press, [in Persian], 716 ppGoogle Scholar
  44. Miles SB, Keefer DK (2009a) Evaluation of CAMEL—comprehensive areal model of earthquake-induced landslides. Eng Geol 104(1):1–15Google Scholar
  45. Miles SB, Keefer DK (2009b) Toward a comprehensive areal model of earthquake-induced landslides. Natural hazards review 10(1):19–28Google Scholar
  46. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236Google Scholar
  47. Motamedi M, Liang RY (2014) Probabilistic landslide hazard assessment using copula modeling technique. Landslides 11(4):565–573Google Scholar
  48. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3):171–191Google Scholar
  49. Norusis MJ (1994) SPSS Advanced Statistics 6.1. SPSS Company, Chicago 606 ppGoogle Scholar
  50. Peng WF, Wang CL, Chen ST, Lee ST (2009) Incorporating the effects of topographic amplification and sliding areas in the modeling of earthquake-induced landslide hazards, using the cumulative displacement method. Comput Geosci 35(5):946–966Google Scholar
  51. Petley DN (2012) Landslides and engineered slopes: protecting society through improved understanding. Landslides and engineered slopes 1:3–13Google Scholar
  52. Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2012a) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz watershed, Iran. In: Terrigenous mass movements. Springer, Berlin, pp 23–49Google Scholar
  53. Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996Google Scholar
  54. Pourghasemi HR, Mohammady M, Pradhan B (2012c) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84Google Scholar
  55. Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk 4(2):93–118Google Scholar
  56. 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 51:350–365Google Scholar
  57. Rapolla A, Paoletti V, Secomandi M (2010) Seismically-induced landslide susceptibility evaluation: application of a new procedure to the island of ischia, Campania region, southern Italy. Eng Geol 114(1):10–25Google Scholar
  58. Regmi NR, Giardino JR, McDonald EV, Vitek JD (2014) A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides 11(2):247–262Google Scholar
  59. Rodrıguez CE, Bommer JJ, Chandler RJ (1999) Earthquake-induced landslides: 1980–1997. Soil Dyn Earthq Eng 18(5):325–346Google Scholar
  60. Shariat Jafari M (2009) Specific risk zonation of landslides in the critical (Central Alborz) zones. National Disaster Mitigation Organization. A specialized workshop of earthquake and landslide, [in Persian], 95 ppGoogle Scholar
  61. Shroder JF (2015) Landslide hazards, risks, and disasters. Elsevier Academic Press, 475 ppGoogle Scholar
  62. Solaymani S, Feghhi K (2003) Report of Surface Faulting and Morphotectonics of Avaj Region Earthquake on June 22, 2002. International Institute of Earthquake Engineering and Seismology, [in Persian], 1–11Google Scholar
  63. Straub D, Schubert M (2008) Modeling and managing uncertainties in rock-fall hazards. Georisk 2(1):1–15Google Scholar
  64. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Google Scholar
  65. Tchalenko JS (1974) Materials for the study of seismotectonics of Iran: North-Central Iran (No. 29). Geological Survey of IranGoogle Scholar
  66. Turner AK, Jayaprakash GP (2012) Introduction. Rockfall characterization and control. Transportation Research Board. National Academy of Sciences, Washington D.C., pp 3–20Google Scholar
  67. Uchida T, Osanai N, Onoda S, Takayama T, Tomura K (2006) A simple method for producing probabilistic seismic shallow landslide hazard maps. In Proceedings of Interpraevent, Universal Academy Press. 2, 529–534Google Scholar
  68. Valagussa A, Frattini P, Crosta GB (2014a) Quantitative probabilistic hazard analysis of earthquake-induced rockfalls. In: Landslide Science for a Safer Geoenvironment, Springer, 213–218Google Scholar
  69. Valagussa A, Frattini P, Crosta GB (2014b) Earthquake-induced rockfall hazard zoning. Eng Geol 182:213–225Google Scholar
  70. Varnes DJ (1978) Slope movement types and processes. Special report 176:11–33Google Scholar
  71. Wasowski J, Del Gaudio V (2000) Evaluating seismically induced mass movement hazard in Caramanico Terme (Italy). Eng Geol 58(3):291–311Google Scholar
  72. Wyllie DC (2014) Rock fall engineering. CRC Press, 270 ppGoogle Scholar
  73. Yesilnacar EK (2005) The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, 423ppGoogle Scholar
  74. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138Google Scholar
  75. Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Proceedings of the 5th international symposium on landslides, Lausanne, Switzerland, AA Balkema Rotterdam, The Netherlands. 2, 1269–1272Google Scholar

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

Authors and Affiliations

  1. 1.Department of Geology, Faculty of Basic SciencesTarbiat Modares UniversityTehranIran
  2. 2.Department of Applied Geology, Faculty of Geological ScienceKharazmi UniversityTehranIran

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