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Landslides

, Volume 11, Issue 2, pp 247–262 | Cite as

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA

  • Netra R. RegmiEmail author
  • John R. Giardino
  • Eric V. McDonald
  • John D. Vitek
Original Paper

Abstract

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).

Keywords

Landslides Logistic regression Sampling technique Susceptibility map West-central Colorado 

References

  1. Allison PD (1999) Logistic regression using the SAS system: theory and application. SAS Institute Inc., CaryGoogle Scholar
  2. Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385CrossRefGoogle Scholar
  3. Ayalew L, Yamagashi H (2005) The application of GIS based logistic regression for landslide susceptibility mapping in Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31CrossRefGoogle Scholar
  4. Barredo JI, Benavidesz A, Herhl J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. International Journal of Applied Earth Observation and Geoinformation 2:9–23CrossRefGoogle Scholar
  5. Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Process 15:1993–2011CrossRefGoogle Scholar
  6. Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology 72:250–271CrossRefGoogle Scholar
  7. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical-models in evaluating landslide hazard. Earth Surf Process Landforms 16:427–445CrossRefGoogle Scholar
  8. Chowdhury RN (1976) Initial stresses in natural slope analysis, rock engineering for foundations and slopes. ASCE Geotechnical Engineering Specialty Conference, Boulder, Colorado, pp 404–414Google Scholar
  9. Chowdhury RN, Bertoldi C (1977) Residual shear tests on soil from two natural slopes. Australian Geomechanics Journal G7:1–9Google Scholar
  10. Clark WA, Hosking PL (1986) Statistical methods for geographers. Wiley, New York, 518 ppGoogle Scholar
  11. Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961CrossRefGoogle Scholar
  12. Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94:325–339CrossRefGoogle Scholar
  13. Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510CrossRefGoogle Scholar
  14. Dai FC, Lee CF (2003) A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landforms 28:527–545CrossRefGoogle Scholar
  15. Dewitte O, Chung C-J, Cornet Y, Daoudi M, Demoulin A (2010) Combining spatial data in landslide reactivation susceptibility mapping: a likelihood ratio-based approach in W Belgium. Geomorphology 122:153–166CrossRefGoogle Scholar
  16. Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece area, Istanbul, Turkey. Environ Geol 51:241–256CrossRefGoogle Scholar
  17. Dunrud RC (1989) Geologic map and coal stratigraphic framework of the Paonia area, Delta and Gunnison Counties, Colorado. US Geological Survey, Coal Investigations Map C-115, Scale 1:50,000.Google Scholar
  18. Egan JP (1975) Signal detection theory and ROC analysis. Academic, New YorkGoogle Scholar
  19. Fenti V, Silvano S, Spagna V (1979) Methodological proposal for an engineering geomorphological map. Forecasting rockfalls in the Alps. Bull Int Assoc Eng Geol 19:134–138CrossRefGoogle Scholar
  20. Fernandez T, Irigaray C, El Hamdouni R, Chacon J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa area (Granada, Spain). Nat Hazard 30:297–308CrossRefGoogle Scholar
  21. Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172–191CrossRefGoogle Scholar
  22. Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161CrossRefGoogle Scholar
  23. Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10:395–415CrossRefGoogle Scholar
  24. Gorum T, Gonencgil B, Gokceoglu C, Nefeslioglu HA (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Nat Hazard 46:323–351CrossRefGoogle Scholar
  25. 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
  26. Guzzetti F, Malamud BD, Turcotte DL, Reichenbach P (2002) Power–law correlations of landslide areas in central Italy. Earth Planet Sci Lett 195:169–183CrossRefGoogle Scholar
  27. Ives JD, Messerli B (1981) Mountain hazard mapping in Nepal: introduction to an Applied Mountain Research Project. Mt Res Dev 1:223–230CrossRefGoogle Scholar
  28. Jaquette C, Wohl E, Cooper D (2005) Establishing a context for river rehabilitation, North Fork Gunnison River, Colorado. Environ Manag 35:593–606CrossRefGoogle Scholar
  29. Johnson DE (1998) Applied multivariate methods for data analysis. Duxbury, BelmontGoogle Scholar
  30. Kienholz H (1978) Maps of geomorphology and natural hazard of Griendelwald, Switzerland, scale 1:10.000. Arct Alp Res 10:169–184CrossRefGoogle Scholar
  31. Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34:223–232CrossRefGoogle Scholar
  32. 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 6:17–26CrossRefGoogle Scholar
  33. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling—a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30CrossRefGoogle Scholar
  34. 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 97:171–191CrossRefGoogle Scholar
  35. Nefeslioglu HA, Gokceoglu C, Sonmez H, Gorum T (2011) Medium scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey). Landslides 8:459–483CrossRefGoogle Scholar
  36. Neuhauser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86:12–24CrossRefGoogle Scholar
  37. Oh H-J, Lee S (2011) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environmental Earth Sciences 62:935–951CrossRefGoogle Scholar
  38. Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptibility mapping in a tropical hilly area. Comput Geosci 37:1264–1276CrossRefGoogle Scholar
  39. 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
  40. Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32:81–100CrossRefGoogle Scholar
  41. Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36:325–334CrossRefGoogle Scholar
  42. Pelletier JD (1997) Kardar-Parisi-Zhang scaling of the height of the convective boundary layer and fractal structure of cumulus cloud fields. Phys Rev Lett 78:2672–2675CrossRefGoogle Scholar
  43. Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84CrossRefGoogle Scholar
  44. Pradhan B (2012) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, http://dx.doi.org/10.1016/j.cageo.2012.08.023.
  45. Pradhan B, Oh H-J, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics, Natural Hazards & Risk 1:199–223CrossRefGoogle Scholar
  46. Regmi NR, Giardino JR, Vitek JD (2010a) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187CrossRefGoogle Scholar
  47. Regmi NR, Giardino JR, Vitek JD (2010b) Assessing susceptibility to landslides: using models to understand observed changes in slopes. Geomorphology 122:25–38CrossRefGoogle Scholar
  48. Regmi NR, Giardino JR, Vitek JD, Dangol V (2010c) Mapping landslide hazards in western Nepal: comparing qualitative and quantitative approaches. Environ Eng Geosci 16:127–142CrossRefGoogle Scholar
  49. Remondo J, Gonzalez-Diez A, De Teran JRD, Cendrero A (2003) Landslide susceptibility models utilising spatial data analysis techniques. A case study from the lower Deba Valley, Guipuzcoa (Spain). Nat Hazard 30:267–279CrossRefGoogle Scholar
  50. Rogers WP (2003) Critical landslides of Colorado—a year 2002 review and priority list. Colorado Geological Survey, Open-File Report OF-02-16, 1map.Google Scholar
  51. Rupke J, Cammeraat E, Seijmonsbergen AC, Vanwesten CJ (1988) Engineering geomorphology of the Widentobel Catchment, Appenzell and Sankt-Gallen, Gallen, Switzerland—a geomorphological inventory system applied to geotechnical appraisal of slope stability. Eng Geol 26:33–68CrossRefGoogle Scholar
  52. Santacana N, Baeza B, Corominas J, De Paz A, Marturia J (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet area (Eastern Pyrenees, Spain). Nat Hazard 30:281–295CrossRefGoogle Scholar
  53. Sezer E, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley, Malaysia. Expert Syst Appl 38:8208–8219CrossRefGoogle Scholar
  54. Søreide K (2009) Receiver-operating characteristic curve analysis in diagnostic, prognostic and predictive biomarker research. J Clin Pathol 62:1–5CrossRefGoogle Scholar
  55. Sterlacchini S, Ballabio C, Blahut J, Masetti M, Sorichetta A (2010) Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology 125:51–61CrossRefGoogle Scholar
  56. Suzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71:303–321CrossRefGoogle Scholar
  57. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science in China Series E-Technological Sciences 240:1285–1293Google Scholar
  58. USGS (2010) Landslide hazards program. http://landslides.usgs.gov/.
  59. Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410CrossRefGoogle Scholar
  60. Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsch 86:404–414CrossRefGoogle Scholar
  61. Western Regional Climate Center (2012) http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?copaon.
  62. Williams CJ, Lee SS, Fisher RA, Dickerman LH (1999) A comparison of statistical methods for prenatal screening for Down syndrome. Applied Stochastic Models and Data Analysis 15:89–101Google Scholar
  63. Wu WM, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31:2097–2110CrossRefGoogle Scholar
  64. Zêzere JL (2002) Landslide susceptibility assessment considering landslide typology. A case study in the area north of Lisbon (Portugal). Natural Hazards and Earth System Science 2:73–82CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Netra R. Regmi
    • 1
    • 2
    Email author
  • John R. Giardino
    • 2
  • Eric V. McDonald
    • 1
  • John D. Vitek
    • 2
  1. 1.Division of Earth and Ecosystem Sciences, Desert Research InstituteRenoUSA
  2. 2.HAARP (High Alpine and Arctic Research Program) and Department of Geology & GeophysicsTexas A&M UniversityCollege StationUSA

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