Journal of Mountain Science

, Volume 14, Issue 7, pp 1241–1261 | Cite as

Hazard and population vulnerability analysis: a step towards landslide risk assessment

  • Franny G. Murillo-García
  • Mauro Rossi
  • Francesca Ardizzone
  • Federica Fiorucci
  • Irasema Alcántara-Ayala


In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlán, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude (area-velocity ratio) and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources (Google Earth, aerial photographs and historical information). Estimations of landslide susceptibility were determined by combining four statistical techniques: (i) logistic regression, (ii) quadratic discriminant analysis, (iii) linear discriminant analysis, and (iv) neuronal networks. A Digital Elevation Model (DEM) of 10 m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology and distance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment (SAVE) model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.


Landslides Susceptibility Hazard Vulnerability Risk 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



Thanks are due to CONACyT for financial support for the research project 156242 and for providing a post-graduate scholarship. The authors are also thankful to the anonymous reviewers, who provided valuable comments that helped to improve the manuscript, and to Ms. Nicola Meza- Williams for the English editing of the text.


  1. Adger WN (2006) Vulnerability. Global Environmental Change 16: 268–281. DOI: 10.1016/j.gloenvcha.2006.02.006CrossRefGoogle Scholar
  2. Akbas SO, Blahut J, Sterlacchini S (2009) Critical assessment of existing physical vulnerability estimation approaches for debris flows. In: Malet JP et al. (eds.), Proceedings of landslide processes: from geomorphologic mapping to dynamic modeling, Strasbourg, 6-7 February 2009. pp 229–233.Google Scholar
  3. Alcántara-Ayala I (2004) Hazard assessment of rainfall induced landsliding in Mexico. Geomorphology 61: 19–40. DOI: 10.1016/j.geomorph.2003.11.004CrossRefGoogle Scholar
  4. Alcántara-Ayala I, López Mendoza M, Melgarejo Palafox G, et al. (2004) Natural hazards and risk communication strategies among indigenous communities: shedding light on accessibility in Mexico’s mountains. Mountain Research and Development 24-4: 298–302. DOI: 10.1659/0276-4741(2004) 024[0298:NHARCS]2.0.CO;2CrossRefGoogle Scholar
  5. Alcántara-Ayala I (2008) On the historical account of disastrous landslides in Mexico: the challenge of risk management and disaster prevention. Advances in Geociences-ADGEO 14: 159–164. DOI: 10.5194/adgeo-14-159-2008CrossRefGoogle Scholar
  6. Alexander D (2005) Vulnerability to landslides. In: Glade T et al. (eds.), Landslide hazard and risk. Wiley. Chichester, UK. pp 175–198.Google Scholar
  7. Bell R, Glade T (2004) Quantitative risk analysis for landslides -Examples from Bildudalur, NW-Iceland. Natural Hazards and Earth System Sciences 4: 117–131. DOI: 10.5194/nhess-4-117-2004CrossRefGoogle Scholar
  8. Birkmann J (2006) Measuring vulnerability to promote disaster-resilient societies: conceptual frameworks and definitions. In: Birkmann J (ed.), Measuring vulnerability to natural hazards. Towards Disaster Resilient Societies. United Nations University Press. Hong Kong. pp 9–54.Google Scholar
  9. Birkmann J, Cardona OD, Carreño ML, et al. (2013) Framing vulnerability, risk and societal responses: the MOVE framework. Natural Hazards 67: 193–211. DOI: 10.1007/s11 069-013-0558-5CrossRefGoogle Scholar
  10. Blaikie P, Cannon T, Davies I, et al. (1994) At Risk. Natural Hazards, People’s Vulnerability and Disasters. Routledge. p 275.Google Scholar
  11. Bohle HG (2001) Vulnerability and Criticality: Perspectives from Social Geography. IHDP Update 2/2001, Newsletter of the International Human Dimensions Programme on Global Environmental Change 1-7.Google Scholar
  12. Cardinali M, Reichenbach P, Guzzetti F, et al. (2002) A geomorphological approach to the estimation of landslide hazard and risk in Umbria, Central Italy. Natural Hazards and Earth System Science 2: 1–16. DOI: 10.5194/nhess-2-57-2002CrossRefGoogle Scholar
  13. Chambers R, Conway G (1992) Sustainable Rural Livelihoods: Practical Concepts for the 21st Century. IDS Discussion Paper 296, Brighton: Institute of Development Studies.Google Scholar
  14. Chung CJF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing 65(12): 1389–1399.Google Scholar
  15. Ciurean R, Schröter D, Glade T (2013) Conceptual Frameworks of Vulnerability Assessments for Natural Disasters Reduction. In: Tiefenbacher J (ed.) Approaches to Disaster Management-Examining the Implications of Hazards, Emergencies and Disasters. InTech. Croatia. pp 3–32.Google Scholar
  16. Cohen, J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37–46.CrossRefGoogle Scholar
  17. Coe JA, Michael JA, Crovelli RA, et al. (2000) Preliminary map showing landslide densities, mean recurrence intervals, and exceedance probabilities as determined from historic records, Seattle, Washington. U.S. Geological Survey Open-File Report 00-303.Google Scholar
  18. Corominas J, Moya J (2008) A review of assessing landslide frequency for hazard zoning purposes. Engineering Geology 102: 193–213. DOI: 10.1016/j.enggeo.2008.03.018CrossRefGoogle Scholar
  19. Corominas J, Copons R, Moya J, et al. (2005) Quantitative assessment of the residual risk in a rockfall protected area. Landslides 2: 343–357. DOI: 10.1007/s10346-005-0022-zCrossRefGoogle Scholar
  20. Crovelli R (2000) Probability models for estimation of number and costs of landslides. U.S. Geological Survey Open File Report 00-249. p 23.Google Scholar
  21. Cutter SL (1996) Vulnerability to environmental hazards. Progress in Human Geography 20: 4529–539CrossRefGoogle Scholar
  22. Dominey-Howes D, Papathoma-Köhle M (2007) Validating a tsunami vulnerability assessment model (the PTVA model) using field data from the 2004 Indian Ocean Tsunami. Natural Hazards 40: 113–136. DOI: 10.1007/s11069-006-0007-9CrossRefGoogle Scholar
  23. Duan M, Gao Q, Wan Y, et al. (2011) Assessing vulnerability and adaptation responses to rainfall related landslides in China, a case study of Enshi Prefecture in Hubei Province. Procedia Environmental Sciences 11: 1379–1385. DOI:10.1016/j.proenv.2011.12.207CrossRefGoogle Scholar
  24. Eidsvig UMK, Papathoma-Köhle M, Dub J, et al. (2014a) Quantification of model uncertainty in debris flow vulnerability assessment. Engineering Geology 181: 15–26. DOI: 10.1016/j.enggeo.2014.08.006CrossRefGoogle Scholar
  25. Eidsvig UMK, McLean A, Vangelsten BV, et al. (2014b) Assessment of socioeconomic vulnerability to landslides using an indicator-based approach: methodology and case studies. Bulletin of Engineering Geology and the Environment 73(2): 307–324. DOI: 10.1007/s10064-014-0571-2CrossRefGoogle Scholar
  26. Fell R (1994) Landslide risk assessment and acceptable risk. Canadian Geotechnical Journal 31: 261–272CrossRefGoogle Scholar
  27. Finlay PJ, Fell R (1997) Landslides: risk perception and acceptance. Canadian Geotechnical Journal 34: 169–188CrossRefGoogle Scholar
  28. Frattini P, Crosta GB, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Engineering Geology 111: 62–72. DOI: 10.1016/j.enggeo.2009.12.004CrossRefGoogle Scholar
  29. Fuchs S, Heiss K, Hübl J (2007) Towards an empirical vulnerability function for use in debris flow risk assessment. Natural Hazards and Earth System Sciences 7: 495–506. DOI: 10.5194/nhess-7-495-2007CrossRefGoogle Scholar
  30. Fuchs S, Kuhlicke C, Volker M (2011) Editorial for the special issue: vulnerability to natural hazards the challenge of integration. Natural Hazards 58: 609–619. DOI: 10.1007/s11069-011-9825-5CrossRefGoogle Scholar
  31. Galli M, Guzzetti F (2007) Landslide vulnerability criteria: a case study from Umbria, Central Italy. Environment Management 40: 649–664. DOI:10.1007/s00267-006-0325-4CrossRefGoogle Scholar
  32. García-Rodríguez MJ, Malpica JA, Benito B, et al. (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95: 172–191. DOI: 10.1016/j.geomorph.2007.06.001CrossRefGoogle Scholar
  33. Glade T, Crozier M (2005) A review of scale dependency in landslide hazard and risk analysis. In: Glade T et al. (eds.), Landslide hazard and risk. John Wiley and Sons. England. pp 75–138. DOI: 10.1002/9780470012659.ch3CrossRefGoogle Scholar
  34. Ghosh S, Carranza E, van Westen CJ, et al. (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131: 35–56. DOI: 10.1016/j.geomorph.2011. 04.019CrossRefGoogle Scholar
  35. Guzzetti F (2005) Landslide Hazzard and Risk Assessment. PhD thesis, Bonn University, Bonn, Germany. p 10Google Scholar
  36. Guzzetti F, Carrara A, Cardinali M, et al. (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31: 181–216. DOI: 10.1016/S0169-555X(99) 00078-1CrossRefGoogle Scholar
  37. Guzzetti F, Reichenbach P, Cardinali M, et al. (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72: 272–299. DOI: 10.1016/j.geomorph.2005. 06.002CrossRefGoogle Scholar
  38. Guzzetti F, Galli M, Reichenbach P, et al. (2006) Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Sciences 6: 115–131, DOI: 1684-9981/nhess/2006-6-115CrossRefGoogle Scholar
  39. Havenith HB, Torgoev A, Schlögel R, et al. (2015) Tien Shan Geohazards Database: Landslide susceptibility analysis. Geomorphology 249: 32–43. DOI:10.1016/j.geomorph. 2015.03.019CrossRefGoogle Scholar
  40. Highland LM (2003) An account of preliminary landslide damage and losses resulting from the February 28, 2001, Nisqually, Washington, Earthquake. U.S. Geological Survey Open-File report 03-211. Available online: http://pubs.usgs. gov/of/2003/ofr-03-211, accessed on 19 April 2017Google Scholar
  41. INEGI (2009) Prontuario de información geográfica municipal de los Estados Unidos Mexicanos. Pahuatlán, Puebla. INEGI. (In Spanish)Google Scholar
  42. INEGI (2011) Panorama sociodemográfico de Puebla. INEGI. (In Spanish)Google Scholar
  43. Kappes MS, Papathoma-Köhle M, Keiler M (2012) Assessing physical vulnerability for multi-hazards using an indicatorbased methodology. Applied Geography 32: 577–590. DOI: 10.1016/j.apgeog.2011.07.002CrossRefGoogle Scholar
  44. Kanungo DP, Arora MP, Sarkar S, et al. (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology 85: 347–366. DOI:10.1016/j.enggeo.2006.03.004CrossRefGoogle Scholar
  45. Kaynia AM, Papathoma-Köhle M, Neuhaüser B, et al. (2008) Probabilistic assessment of vulnerability to landslide: application to the village of Lichtenstein, Baden-Württemberg, Germany. Engineering Geology 101: 33–48. DOI: 10.1016/j.enggeo.2008.03.008CrossRefGoogle Scholar
  46. Komac M (2006) A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia. Geomorphology 74: 17–28. DOI: 10.1016/j.geomorph.2005.07.005CrossRefGoogle Scholar
  47. Leone F, Aesté JP, Leroi E (1996) Vulnerability assessment of elements exposed to mass-movement: working toward a better risk perception. In: Senneset K (ed.), Landslides Gliseements de Terrain. Balkema. Rotterdam, Holland. pp 263–270.Google Scholar
  48. Li Z, Nadim F, Huang H, Uzielli M, et al. (2010) Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides 7(2): 125–134. DOI: 10.1007/s10346-009-0190-3CrossRefGoogle Scholar
  49. Liu X, Lei J (2003) A method for assessing regional debris flow risk: an application in Zhaotong of Yunnan province (SW China). Geomorphology 52: 181–191. DOI: 10.1016/S0169-555X(02)00242-8CrossRefGoogle Scholar
  50. Lu P, Catani F, Tofani V, et al. (2014) Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry. Landslides 11: 685–696. DOI: 10.1007/s10346-013-0432-2CrossRefGoogle Scholar
  51. Marchesini I, Rossi M, Alvioli M, et al. (2012) WPS tools to support geological and geomorphological mapping. In: Ertz O et al. (eds.) Proceedings of the OGRS 2012 (Open Source Geospatial Research & Education Symposium). Yverdon-les-Bains Switzerland. pp 280–287.Google Scholar
  52. Mavrouli O, Corominas J (2010) Rockfall vulnerability assessment for reinforced concrete buildings. Natural Hazards and Earth System Science 10 (10):2055–2066. DOI: 10.5194/nhess-10-2055-2010CrossRefGoogle Scholar
  53. Mejia-Navarro M, Wohl LEE, Oaks SD (1994) Geological hazards, vulnerability, and risk assessment using GIS: model for Glenwood Springs, Colorado. Geomorphology 10: 331–354CrossRefGoogle Scholar
  54. Maquaire O, Weber C, Thiery Y, et al. (2004) Current practices and assessment tools of landslide vulnerability in mountainous basins. Identification of exposed elements with a semi-automatic procedure. In: Lacerda W et al. (eds.) Landslides evaluation and stabilization, Proceedings of the 9th International Symposium on Landslides, Río de Janeiro, Brazil. Balkema, Rotterdam. pp 171–176.Google Scholar
  55. Michael-Leiba M, Baynes F, Scott G (2000) Quantitative landslide risk assessment of Cairns, Australia. In: Bromhead E et al. (eds.), Landslides in research, Theory and practice. Thomas Telford.London, UK. pp 1059–1064Google Scholar
  56. Baynes F, Scott G, et al. (2003) Regional landslide risk to the cairns community. Natural Hazards 30: 233–249. DOI: 10.1023/A: 1026122518661CrossRefGoogle Scholar
  57. Morales-Manilla LM (2010) A common spatial approach to vulnerability assessment. GI Forum 2010, July 6-9-2010. Salzburg, Austria.Google Scholar
  58. Mousavi M, Omidvar B, Ghazban F, et al. (2011) Quantitative risk analysis for earthquake-induced landslides—Emamzadeh Ali, Iran. Engineering Geology 122: 191–203. DOI: 10.1016/j. enggeo.2011.05.010CrossRefGoogle Scholar
  59. Murillo-García FG, Alcántara-Ayala I (2015) Landslide susceptibility analysis and mapping using statistical multivariate techniques: Pahuatlán, Puebla, Mexico. In: Wu W (ed.) Recent advances in modelling landslides and debris flows. Springer. Switzerland. pp 179–194Google Scholar
  60. Murillo-García F, Alcántara-Ayala I, Ardizzone F, et al. (2015) Satellite stereoscopic pair images of very high resolution: a step forward for the development of landslide inventories. Landslides 12(2): 277–291. DOI: 10.1007/s10346-014-0473-1CrossRefGoogle Scholar
  61. Negulescu C and Foerster E (2010) Parametric studies and quantitative assessment of the vulnerability of a RC frame building exposed to differential settlements. Natural Hazards and Earth System Sciences. 10: 1781–1792. DOI: 10.5194/nhess-10-1781-2010CrossRefGoogle Scholar
  62. Neumayer E, Plümper T (2007) The gendered nature of natural disasters: the impact of catastrophic events on the gender gap in life expectancy, 1981–2002. Annals of the Association of American Geographers 97(3): 551–566. DOI: 10.1111/j.1467-8306.2007.00563.xCrossRefGoogle Scholar
  63. Pamungkas A, Bekessy S, Lane R (2014) Vulnerability Modelling to Improve Assessment Process on Community Vulnerability. Procedia-Social and Behavioral Sciences 135: 159–166.CrossRefGoogle Scholar
  64. Papathoma-Köhle M, Neuhäuser B, Ratzinger K, et al. (2007) Elements at risk as a framework for assessing the vulnerability of communities to landslides. Natural Hazards and Earth System Sciences 7: 765–779. DOI: 10.5194/nhess-7-765-2007CrossRefGoogle Scholar
  65. Papathoma-Köhle M, Kappes M, Keiler M, et al. (2011) Physical vulnerability assessment for alpine hazards: state of the art and future needs. Natural Hazards 58: 645–680. DOI: 10.1007/s11069-010-9632-4CrossRefGoogle Scholar
  66. Papathoma-Köhle M, Keiler R, Totschnig T, et al. (2012) Improvement of vulnerability curves using data from extreme events: debris flow event in South Tyrol. Natural Hazards 64: 2083–2105. DOI: 10.1007/s11069-012-0105-9CrossRefGoogle Scholar
  67. Papathoma-Köhle M, Zischg A, Fuchs S, et al. (2015) Loss estimation for landslides in mountain areas -An integrated toolbox for vulnerability assessment and damage documentation. Environmental Modelling & Software 63: 156–169. DOI: 10.1016/j.envsoft.2014.10.003CrossRefGoogle Scholar
  68. Parkash S (2013) Capacity development for landslide risk reduction in India. In: Sassa K et al. (eds.), Landslides: global risk preparedness. Springer. Berlin, Germany. pp 371–385.Google Scholar
  69. Peng L, Xu S, Hou J, et al. (2015) Quantitative risk analysis for landslides: the case of the Three Gorges area, China. Landslides 12: 943–960. DOI: 10.1007/s10346-014-0518-5CrossRefGoogle Scholar
  70. Petrucci O, Gullà G (2010) A simplified method for assessing landslide damage indices. Natural Hazards 52: 539–560. DOI: 10.1007/s11069-009-9398-8CrossRefGoogle Scholar
  71. Quan Luna B, Blahut J, Van Westen CJ, et al. (2011) The application of numerical debris flow modelling for the generation of physical vulnerability curves. Natural Hazards and Earth System Sciences 11: 2047–2060. DOI: 10.5194/nhess-11-2047-2011CrossRefGoogle Scholar
  72. Ragozin AL (1996) Modern problems and quantitative methods of landslide risk assessment. In: Senneset K (ed.), Landslides-Glissements de Terrain, Balkema. Rotterdam, Holland, pp. 339–344.Google Scholar
  73. Reichenbach P, Galli M, Cardinali M, et al. (2005) Geomorphologic mapping to assess landslide risk: concepts, methods and applications in the Umbria Region of central Italy. In: Glade T et al. (eds.), Landslide risk assessment. John Wiley and Sons. England. pp 75–138. DOI: 10.1002/97804700 12659.ch3Google Scholar
  74. Roberds W (2005) Estimating temporal and spatial variability and vulnerability. In: Hungr O et al. (eds.), Landslide Risk Management. Taylor & Francis Group U.S. pp 129–157.Google Scholar
  75. Rossi M, Guzzetti F, Reichenbach P, et al. (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114: 129–142. DOI: 10.1016/j.geomorph. 2009.06.020CrossRefGoogle Scholar
  76. Rossi M, Ardizzone F, Cardinalli M, et al. (2012) A tool for the estimation of the distribution of landslide area in R. Geophysical Research Abstracts, 14, EGU2012-9438-1. Vienna, Austria. p 9438.Google Scholar
  77. Sánchez-Rojas LE, De la Callejera-Moctezuma AE (2004) Carta Geológico-Minera Pahuatlán F14-D73. Servicio Geológico Mexicano, Escala 1:50 000. (In Spanish)Google Scholar
  78. Sajinkumar KS, Anbazhagan S, Rani VR, et al. (2014) A paradigm quantitative approach for a regional risk assessment and management in a few landslide prone hamlets along the windward slope of Western Ghats, India. International Journal of Disaster Risk Reduction 7: 142–153. DOI: 10.1016/j.ijdrr.2013.10.004CrossRefGoogle Scholar
  79. Shrestha A (2005) Vulnerability assessment of weather disasters in Syangja District, Nepal: A case study of Putalibazar Municipality, advanced institute on vulnerability to global environmental dhange/global change system for analysis research and training. Kathmandu: START and Department of Hydrology, Nepal.Google Scholar
  80. Sterlacchini S, Frigerio S, Giacomelli P, et al. (2007) Landslide risk analysis: a multi-disciplinary methodological approachNatural Hazards and Earth System Sciences 7: 657–675. DOI: 10.5194/nhess-7-657-2007Google Scholar
  81. Thanapackiam P, Khairulmaini OS, Fauza AG (2012) Vulnerability and adaptive capacities to slope failure threat: a study of the Klang Valley Region. Natural Hazards 62: 805–826. DOI: 10.1007/s11069-012-0108-6CrossRefGoogle Scholar
  82. Totschnig R, Sedlacek W, Fuchs S (2011) A quantitative vulnerability function for fluvial sediment transport. Natural Hazards 58(2): 681–703. DOI: 10.1007/s11069-010-9623-5CrossRefGoogle Scholar
  83. Tsao TC, Hsu WK, Cheng CT, et al. (2010) A preliminary study of debris flow risk estimation and management in Taiwan. In: Chen SC (ed.), International Symposium Interpraevent in the Pacific Rim-Taipei, 26-30 Apr. Internationale Forschungsgesellschaft Interpraevent, Klagenfurt, Austria. pp 930–939.Google Scholar
  84. Turner IIBL, Kasperson RE, Matsone PA, et al. (2003) A framework for vulnerability analysis in sustainability science. PNAS 100(14): 8074–8079. DOI: 10.1073/pnas.1231335100CrossRefGoogle Scholar
  85. UN/ISDR (2004) Living with Risk. A Global Review of Disaster Reduction Initiatives. 2004 version. UN Publications, Geneva.Google Scholar
  86. Uzielli M, Nadim F, Lacasse S, et al. (2008) A conceptual framework for quantitative estimation of physical vulnerability to landslides. Engineering Geology 102: 251–256. DOI: 10.1016/j.enggeo.2008.03.011CrossRefGoogle Scholar
  87. Varnes DJ (1978) Slope movement types and processes. In: Schuster RL et al. (eds.), Landslides: Analysis and Control Special Report 176, TRB, National Research Council, Washington, DC, U.S. pp 11–33.Google Scholar
  88. Van Den Eeckhaut M, Marre A, Poesen J (2010) Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France).Geomorphology 115: 141–155. DOI: 10.1016/j.geomorph. 2009. 09.042Google Scholar
  89. Walters V, Gaillard JC (2014) Disaster risk at the margins: Homelessness, vulnerability and hazards. Habitat International 44: 211–219. DOI: 10.1016/j.habitatint.2014.06.006CrossRefGoogle Scholar
  90. Wilches-Chaux G (1998) Auge, caída y levantada de Felipe Pinillo, mecánico y soldador o yo voy a correr el riesgo. Red de Estudios Sociales en Prevención de Desastres en América Latina. Peru. p 103. (In Spanish)Google Scholar
  91. Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112: 190–204. DOI: 10.1016/j.geomorph.2009.06.002CrossRefGoogle Scholar
  92. Xu C, Xu X, Dai F, et al. (2012) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences 46: 317–329. DOI: 10.1016/j.cageo. 2012.01.002CrossRefGoogle Scholar
  93. Xu C, Xu X, Yao X, et al. (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(3): 441–461. DOI: 10.1007/s10346-013-0404-6CrossRefGoogle Scholar
  94. Xu C, Xu X, Shyu JBH (2015) Database and spatial distribution of landslides triggered by the Lushan, China Mw 6.6 earthquake of 20 April 2013. Geomorphology 248: 77–92. DOI: 10.1016/j.geomorph.2015.07.002CrossRefGoogle Scholar
  95. Zezere JL, Garcia RAC, Oliveira SC, et al. (2008) Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal). Geomorphology 94: 467–495. DOI: 10.1016/j.geomorph.2006.10.040CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Franny G. Murillo-García
    • 1
  • Mauro Rossi
    • 2
  • Francesca Ardizzone
    • 2
  • Federica Fiorucci
    • 2
  • Irasema Alcántara-Ayala
    • 3
  1. 1.Postgraduate in GeographyNational Autonomous University of Mexico (UNAM)Mexico CityMexico
  2. 2.IRPI CNRPerugiaItaly
  3. 3.Institute of GeographyNational Autonomous University of Mexico (UNAM)Mexico CityMexico

Personalised recommendations