Advertisement

Geotechnical and Geological Engineering

, Volume 37, Issue 4, pp 3085–3111 | Cite as

A Comparison Among ANFIS, MLP, and RBF Models for Hazard Analysis of Rockfalls Triggered by the 2004 Firooz Abad-Kojour, Iran, Earthquake

  • Vahid Bagheri
  • Ali UromeihyEmail author
  • Seyed Mahmood Fatemi Aghda
Original Paper
  • 103 Downloads

Abstract

Rockfall hazard is a very common phenomenon mainly occurring in mountainous slopes, coastal cliffs, volcanoes, riverside, and trenches. In the present research, a model is developed for hazard analysis of seismic rockfalls on a regional scale. For this purpose, three models including Adaptive Neuro-Fuzzy Inference System (ANFIS), multilayer perceptron artificial neural network (MLP), and radial basis function artificial neural network (RBF) were utilized. Firooz Abad-Kojour earthquake of 2004 was used as the benchmark and the model base. The rockfall-susceptible zones predicted by ANFIS, MLP, and RBF methods were compared with the database (distribution map) of seismic rockfalls. The results showed a good overlap between MLP-predicted rockfall hazard zones and database (distribution map) of seismic rockfalls. To evaluate the statistical results of ANFIS, MLP, and RBF models, the verification parameters with high accuracy such as density ratio, quality sum (Qs), and Receiver Operating Characteristic Curve were employed. By analyzing the hazard maps and considering the Qs index obtained by ANFIS (26.76) and MLP (49.19), and RBF (13.84), it could be observed that the calculated Qs of MLP were higher than those of ANFIS and RBF. Moreover, based on the obtained value of the area under the curve from ANFIS (0.984), MLP (0.986), and RBF methods (0.884), it is seen that the MLP network, compared to ANFIS and RBF models, provided a higher accuracy in hazard analysis of rockfalls caused by the earthquake of Firooz Abad-Kojour of 2004.

Keywords

Seismic rockfalls Hazard analysis Firooz Abad-Kojour earthquake ANFIS MLP RBF 

Notes

References

  1. Amini Hosseini K, Mahdavifar MR, Bakhshayesh MK, Khazaie B, Azadi A, Bidar AS, Ravanfar, SO, Kamalpour F, Rakhshandeh M, Banki S (2004) Earthquake-induced landslide identification report of the 2004/05/28 Firooz Abad-Kojour earthquake. Seismology and Earthquake Engineering Research Journal, 7(2):21–36 (in Persian) Google Scholar
  2. Arias A (1970) A measure of the earthquake intensity in seismic design for nuclear power plants. Massachusetts Inst of Tech Press, Cambridge, pp 438–468Google Scholar
  3. Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Phys ChemEarth Parts A/B/C 35(3):162–171CrossRefGoogle Scholar
  4. 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):595–625CrossRefGoogle Scholar
  5. 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 88:307–327CrossRefGoogle Scholar
  6. 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):383–408CrossRefGoogle Scholar
  7. Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411–423CrossRefGoogle Scholar
  8. Clague JJ, Stead D (2012) Landslides: types, mechanisms and modeling. Cambridge University Press, Cambridge, p 436CrossRefGoogle Scholar
  9. 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). Environ Earth Sci 63(2):397–406CrossRefGoogle Scholar
  10. 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):343–357CrossRefGoogle Scholar
  11. Corominas J, Mavrouli O, Ruiz-Carulla R (2017) Rockfall occurrence and fragmentation. In: Workshop on world landslide forum. Springer, pp 75–97Google Scholar
  12. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation. Transportation research board, special report 247, pp 36–75Google Scholar
  13. Del Gaudio V, Wasowski J (2004) Time probabilistic evaluation of seismically induced landslide hazard in Irpinia (Southern Italy). Soil Dyn Earthq Eng 24(12):915–928CrossRefGoogle Scholar
  14. 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):557–569CrossRefGoogle Scholar
  15. Fatemi Aghda SM, Bagheri V (2015) Evaluation of earthquake-induced landslides hazard zonation methods: a case study of Sarein, Iran, earthquake (1997). Arab J Geosci 8(9):7207–7227CrossRefGoogle Scholar
  16. Fatemi Aghda SM, Sarikhani R, Teshneh Lab M (2003) Landslide zonation in Talesh Area using the expert systems (MLP network). J Eng Geol 1(2):179–192 [in Persian] Google Scholar
  17. Gee MD (1992) Classification of landslide hazard zonation methods and a test of predictive capability. In: Proceedings of 6th international symposium on landslides, vol 2, Christchurch, New Zealand, pp 947–952Google Scholar
  18. Gosar A (2017) Earthquake-induced rockfalls caused by 1998 Mw 5.6 earthquake in Krn Mountains (NW Slovenia) and ESI 2007 intensity scale. In: Workshop on world landslide forum. Springer, pp 131–139Google Scholar
  19. Hagan MT, Demuth HB, Beale MH, Jesús OD (2014) Neural network design, 2nd edn. PWS Publishing, Boston, p 1012Google Scholar
  20. 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):224–238CrossRefGoogle Scholar
  21. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  22. Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58(3):271–289CrossRefGoogle Scholar
  23. Keefer DK (1984) Landslides caused by earthquakes. Geol Soc Am Bull 95(4):406–421CrossRefGoogle Scholar
  24. Keefer DK (2000) Statistical analysis of an earthquake-induced landslide distribution -the 1989 Loma Prieta, California event. Eng Geo J 58:231–249CrossRefGoogle Scholar
  25. Keefer DK (2013) Landslides generated by earthquakes: immediate and long-term effects. Treatise Geomorphol 5:250–266CrossRefGoogle Scholar
  26. Kia SM (2014) Fuzzy logic in MATLAB. Kian Academy Press, Tehran, p 304 [in Persian] Google Scholar
  27. Lari S, Frattini P, Crosta GB (2014) A probabilistic approach for landslide hazard analysis. Eng Geol 182:3–14CrossRefGoogle Scholar
  28. Lee CT, Huang CC, Lee JF, Pan KL, Lin ML, Dong JJ (2008) A statistical approach to earthquake-induced landslide susceptibility. Eng Geol 100(1–2):43–58CrossRefGoogle Scholar
  29. Mahdavifar MR (2006) Analytical evaluation and design of the system (GIS) for seismic landslides hazard management in Iran. Ph.D thesis. International Institute of Earthquake Engineering and Seismology, pp 213. [in Persian] Google Scholar
  30. Mahdavifar MR, Jafari MK, Zolfaghari MR (2007) The attenuation of Arias intensity in Alborz and Central Iran. The fifth international conference on seismology and earthquake engineering, Tehran, Iran, pp 7Google Scholar
  31. 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. J Seismol Earthq Eng 18(2):101–116Google Scholar
  32. Marzorati S, Luzi L, De Amicis M (2002) Rock falls induced by earthquakes: a statistical approach. Soil Dyn Earthq Eng 22(7):565–577CrossRefGoogle Scholar
  33. Massey CI, MacSaveney MJ, Richards L (2015) Characteristics of some rockfalls triggered by the 2010/2011 Canterbury earthquake sequence, New Zealand. Eng Geol Soc Territory 2:1943–1948CrossRefGoogle Scholar
  34. 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):17–26CrossRefGoogle Scholar
  35. Menhaj MB (2003) Basics of artificial networks: computation intelligence. Amir Kabir University Press (Tehran Polytechnic), Tehran, p 716Google Scholar
  36. Miles SB, Keefer DK (2007) Comprehensive areal model of earthquake-induced landslides: technical specification and user guide. U.S. Geological Survey Open-File Report 2007–1072, pp 69Google Scholar
  37. Miles SB, Keefer DK (2009a) Evaluation of CAMEL-comprehensive areal model of earthquake-induced landslides. Eng Geol 104(1):1–15CrossRefGoogle Scholar
  38. Miles SB, Keefer DK (2009b) Toward a comprehensive areal model of earthquake-induced landslides. Nat Hazards Rev 10(1):19–28CrossRefGoogle Scholar
  39. 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 61:221–236CrossRefGoogle Scholar
  40. Motamedi M, Liang RY (2013) Probabilistic landslide hazard assessment using Copula modeling technique. Landslides 11(4):565–573CrossRefGoogle Scholar
  41. 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):171–191CrossRefGoogle Scholar
  42. Norusis MJ (1994) SPSS advanced statistics 6.1. SPSS Company, Chicago, Illinois, p 606Google Scholar
  43. Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276CrossRefGoogle Scholar
  44. 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):946–966CrossRefGoogle Scholar
  45. Polat K, Güneş S (2006) Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation. Digit Signal Process 16(6):889–901CrossRefGoogle Scholar
  46. Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84CrossRefGoogle Scholar
  47. 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):965–996CrossRefGoogle Scholar
  48. Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2012c) Landslide susceptibility mapping using a spatial multi-criteria evaluation model at Haraz Watershed, Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49CrossRefGoogle Scholar
  49. 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. Geomat Nat Hazards Risk 4(2):93–118CrossRefGoogle Scholar
  50. 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
  51. Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177CrossRefGoogle Scholar
  52. 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):10–25CrossRefGoogle Scholar
  53. 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):247–262CrossRefGoogle Scholar
  54. Rodrıguez CE, Bommer JJ, Chandler RJ (1999) Earthquake-induced landslides: 1980–1997. Soil Dyn Earthq Eng 18(5):325–346CrossRefGoogle Scholar
  55. 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, pp 95 [in Persian] Google Scholar
  56. Shroder JF (2015) Landslide hazards, risks, and disasters. Elsevier Academic Press, Amsterdam, p 475Google Scholar
  57. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Ltd, pp 278Google Scholar
  58. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293CrossRefGoogle Scholar
  59. 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, vol 2. Universal Academy Press, pp 529–534Google Scholar
  60. Valagussa A, Frattini P, Crosta GB (2014a) Quantitative probabilistic hazard analysis of earthquake-induced rockfalls. In: Sassa K, Canuti P, Yin Y (eds) Landslide science for a safer geoenvironment. Springer, Cham, pp 213–218CrossRefGoogle Scholar
  61. Valagussa A, Frattini P, Crosta GB (2014b) Earthquake-induced rockfall hazard zoning. Eng Geol 182:213–225CrossRefGoogle Scholar
  62. Varnes, D.J. (1978). Slope movements and types and processes. In: Landslides: analysis and control, transportation research. Board national, special report, vol 176, pp 11–33Google Scholar
  63. Wasowski J, Del Gaudio V (2000) Evaluating seismically induced mass movement hazard in Caramanico Terme (Italy). Eng Geol 58(3):291–311CrossRefGoogle Scholar
  64. Wilson RC (1993) Relation of arias intensity to magnitude and distance in California. U.S Geological Survey Open-File report 1993–556, pp 41Google Scholar
  65. Wyllie DC (2014) Rockfall engineering. CRC Press, Boca Raton, p 270Google Scholar
  66. Yesilnacar, E. K. (2005). The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp 423Google Scholar
  67. Ying LC, Pan MC (2008) Using adaptive network-based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49(2):205–211CrossRefGoogle Scholar
  68. Zare M (2004) Seismology and earthquake engineering aspects of the 2004/05/28 Firooz Abad-Kojour earthquake with magnitudes Mw = 6.2. Seismol Earthq Eng Res J 7(2):45–57Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vahid Bagheri
    • 1
  • Ali Uromeihy
    • 1
    Email author
  • Seyed Mahmood Fatemi Aghda
    • 2
  1. 1.Department of Geology, Faculty of Basic SciencesTarbiat Modares UniversityTehranIran
  2. 2.Department of Applied Geology, Faculty of Geological ScienceKharazmi UniversityTehranIran

Personalised recommendations