Landslides

, Volume 2, Issue 4, pp 280–290

Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island

Original Article

Abstract

On the basis of 1,834 landslide data for Hong Kong Island (HKI), landslide susceptibility maps were generated using logistic regression and GIS. Regional bias of the landslide inventory is examined by dividing the whole HKI into a southern and a northern region, separated by an east-west trending water divide. It was found that the susceptibility map of southern HKI generated by using the southern data differs significantly from that generated by using northern data, and similar conclusion can be drawn for the northern HKI. Therefore, a susceptibility map of HKI was established based on regional data analysis, and it was found to reflect closely the spatial distributions of historical landslides. Elevation appears to be the most dominant factor in controlling landslide occurrence, and this probably reflects that human developments are concentrated at certain elevations on the island. Classification plot, goodness of fit, and occurrence ratio were used to examine the reliability of the proposed susceptibility map. The size of landslide susceptible zones varies depending on the data sets used, thus this demonstrates that the historical landslide data may be biased and affected by human activities and geological settings on a regional basis. Therefore, indiscriminate use of regional-biased data should be avoided.

Keywords

Landslide data Inventory GIS Regional bias Hong Kong China 

References

  1. 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(1/2):15–31CrossRefGoogle Scholar
  2. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81CrossRefGoogle Scholar
  3. Cancelli A, Crosta G (1994) Hazard and risk assessment in rockfall prone areas. In: Skipp BO (ed) Risk and reliability in ground engineering. Thomas Telford, London, pp 177–190Google Scholar
  4. Chau KT, Lo KH (2004) Hazard assessment of debris flow for Leung King Estate of Hong Kong by incorporating GIS with numerical simulations. Nat Hazards Earth Syst Sci 4(1):103–116CrossRefGoogle Scholar
  5. Chau KT, Wong RHC, Lui J, Lee CF (2003) Rockfall hazard analysis for Hong Kong based on rockfall inventory. Rock Mech Rock Eng 36(5):383–408CrossRefGoogle Scholar
  6. Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP (2004a) Landslide hazard analysis for Hong Kong based on landslide inventory and GIS. Comput Geosci 30:429–443CrossRefGoogle Scholar
  7. Chau KT, Tang YF, Wong RHC (2004b) GIS based rockfall hazard map for Hong Kong. Int J Rock Mech Mining Sc 41(3):530CrossRefGoogle Scholar
  8. Coppock JT (1995) GIS and natural hazards: An overview from a GIS perspective. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht, pp 21–34Google Scholar
  9. Corominas J, Santacana N (2003) Stability analysis of the Vallcebre translational slide, Eastern Pyrenees (Spain) by means of a GIS. Nat Hazards 30(3):473–485CrossRefGoogle Scholar
  10. Corominas J, Copons R, Vilaplana JM, Altimir J, Amigó J (2003) Integrated Landslide Susceptibility Analysis and Hazard Assessment in the Principality of Andorra. Nat Hazards 30(3):421–435CrossRefGoogle Scholar
  11. Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: A case study. Can Geotech J 38(5):911–923CrossRefGoogle Scholar
  12. Dai FC, Lee CF (2002) Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3/4):213–228CrossRefGoogle Scholar
  13. 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(5):527–545CrossRefGoogle Scholar
  14. Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391CrossRefGoogle Scholar
  15. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: An overview. Eng Geol 64(1):65–87CrossRefGoogle Scholar
  16. Einstein HH (1988) Landslide risk assessment procedure. In: Proceedings of the 5th international symposium on landslides, vol 2, Balkema, Rotterdam, pp 1075–1090Google Scholar
  17. Einstein HH (1997) Landslide risk—Systematic approaches to assessment. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam, pp 25–50Google Scholar
  18. Fell R, Hartford D (1997) Landslide risk management. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam, pp 51–109Google Scholar
  19. Fernandez CI, Del Castillo TF, El Hamdouni R, Montero JC (1999) Verification of landslide susceptibility mapping: A case study. Earth Surf Process Landforms 24(6):537–544CrossRefGoogle Scholar
  20. Fyfe JA, Shaw R, Campbell SDG, Lai KW, Kirk PA (2000) The quaternary geology of Hong Kong. Geotechnical Engineering Office, Hong Kong, 210 ppGoogle Scholar
  21. Geotechnical Engineering Office (GEO) (1996) Compilation of a database on landslide consequence. Submitted by Mitchell, McFarlane, Brentnall & Partners Int. Ltd., Agreement No. GEO 8/95, Final report for Special Project Division, GEO, 310 ppGoogle Scholar
  22. Gritzner ML, Marcus WA, Aspinall R, Custer SG (2001) Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37(1/2):149–165CrossRefGoogle Scholar
  23. Hansen A (1984) Landslide hazard analysis. In: Brunsden D, Prior DB (eds) Slope instability, Chapter 13. Wiley, New York, pp 523–602Google Scholar
  24. Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York, 373 ppGoogle Scholar
  25. Hungr O (1997) Some methods of landslides hazard intensity mapping. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam, pp 215–226Google Scholar
  26. Kleinbaum DG, Klein M (2002) Logistic regression: A self-learning text. Springer-Verlag, New York, 513 ppGoogle Scholar
  27. Larsen MC, Parks JE (1997) How wide is a road? The association of roads and mass-wasting in a forested mountain environment. Earth Surf Process Landforms 22:835–848CrossRefGoogle Scholar
  28. Lee CF, Ye H, Yeung MR, Shan X, Chen G (2001) AIGIS-based methodology for natural terrain landslide susceptibility mapping in Hong Kong. Episodes 24(3):150–159Google Scholar
  29. Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manage 34(2):223–232CrossRefPubMedGoogle Scholar
  30. Lee S (2005a) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26(7):1477–1491CrossRefGoogle Scholar
  31. Lee S (2005b) Application and cross-validation of spatial logistic multiple regression for landslide susceptibility analysis. Geosci J 9(1):63–71Google Scholar
  32. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113CrossRefGoogle Scholar
  33. Lee S, Choi J, Woo I (2004a) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: A case study in Boun, Korea. Geosci J 8(1):51–60CrossRefGoogle Scholar
  34. Lee S, Choi J, Min K (2004b) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25(11):2037–2052CrossRefGoogle Scholar
  35. Lee S, Ryu JH, Won JS, Park HJ (2004c) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3/4):289–302CrossRefGoogle Scholar
  36. Leroi E (1997) Landslide risk mapping: Problems, limitation and developments. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam, pp 239–250Google Scholar
  37. Lu P, Rosenbaum MS (2003) Artificial neural networks and Grey Systems for the prediction of slope stability. Nat Hazards 30(3):383–398CrossRefGoogle Scholar
  38. Luzi L, Pergalani F, Terlien MTJ (2000) Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems. Eng Geol 58(3/4):313–336CrossRefGoogle Scholar
  39. Marquinez J, Duarte RM, Farias P, Sanchez MJ (2003) Predictive GIS-based model of rockfall activity in Mountain Cliffs. Nat Hazards 30(3):341–360CrossRefGoogle Scholar
  40. Menard SW (2002) Applied logistic regression analysis, 2nd edn. Sage, Thousand Oaks, CA, 111 ppGoogle Scholar
  41. Menendez-Duarte R, Marquinez J, Devoli G (2003) Slope instability in Nicaragua triggered by Hurricane Mitch: Distribution of shallow mass movements. Environ Geol 44(3):290–300CrossRefGoogle Scholar
  42. Miles SB, Keefer DK (1999) Evaluation of seismic slope-performance models using a regional case study. Environ Eng Geosci 6(1):25–39CrossRefGoogle Scholar
  43. Myster RW, Thomlinson JR, Larsen MC (1997) Predicting landslide vegetation in patches on landscape gradients in Puerto Rico. Landscape Ecol 12(5):299–307CrossRefGoogle Scholar
  44. Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3/4):331–343CrossRefGoogle Scholar
  45. Pampel FC (2000) Logistic regression: A primer. Sage, Thousand Oaks, CA, 86 ppGoogle Scholar
  46. Refice A, Capolongo D (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci 28(6):735–749CrossRefGoogle Scholar
  47. Remondo J, Gonzalez A, De Teran JRD, Cendrero A, Fabbri A, Chung CJF (2003) Validation of landslide susceptibility maps: Examples and applications from a case study in northern Spain. Nat Hazards 30(3):437–449CrossRefGoogle Scholar
  48. Rowbotham DN, Dudycha D (1998) GIS modelling of slope stability in Phewa Tal watershed, Nepal. Geomorphology 26(1–3):151–170CrossRefGoogle Scholar
  49. Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2(1):61–69CrossRefGoogle Scholar
  50. 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 Hazards 30(3):281–295CrossRefGoogle Scholar
  51. Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Eng Remote Sens 70(5):617–625Google Scholar
  52. Sassa K, Wang G, Fukuoka H, Wang F, Ochiai T, Sugiyama M, Sekiguchi T (2004) Landslide risk evaluation and hazard zoning for rapid and long-travel landslides in urban development areas. Landslides 1(3):221–235CrossRefGoogle Scholar
  53. Varnes DJ (1984) Landslide hazard zonation: A review of principles and practice. UNESCO, France, pp 1–63Google Scholar
  54. Wang HB, Sassa K (2005) Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environ Geol 47(7):956–966CrossRefGoogle Scholar
  55. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3/4):251–266CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Department of Civil and Structural EngineeringThe Hong Kong Polytechnic UniversityHung HomHong Kong

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