Natural Hazards

, 43:223 | Cite as

Vulnerability assessment of rainfall-induced debris flows in Taiwan

Original Paper


A GIS-based decision support system, which incorporates local topographic and rainfall effects on debris flow vulnerability is developed. Rainfall at a scale compatible with the digital elevation model resolution is obtained using a neural network with a wind-induced topographic effect and rainfall derived from satellite rain estimates and an adaptive inverse distance weight method (WTNN). The technique is tested using data collected during the passage of typhoon Tori-Ji on July 2001 over central Taiwan. Numerous debris flows triggered by the typhoon were used as control for the study. Our results show that the WTNN technique outperforms other interpolation techniques including adaptive inversed distance weight (AIDW), simple kriging (SK), co-kriging, and multiple linear regression using gauge, and topographic parameters. Multiple remotely-sensed, fuzzy-based debris-flow susceptibility parameters are used to describe the characteristics of watersheds. Non-linear, multi-variant regressions using the WTNN derived rainfall and topography factors are derived using self-organizing maps (SOM) for the debris flow vulnerability assessment. An index of vulnerability representing the degrees of hazard is implemented in a GIS-based decision support system by which a decision maker can assess debris flow vulnerability.


Debris flow Spatial interpolation Susceptibility Vulnerability assessment Decision support system Satellite rainfall 


  1. Antonic O, Legovic T (1999) Estimating the direction of an unknown air pollution source using a digital elevation model and a sample of deposition. Ecol Model 124:85–95CrossRefGoogle 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(1–2):15–31CrossRefGoogle Scholar
  3. Bindlish R, Barros PA (2000) Disaggregation of rainfall for one-way coupling of atmospheric and hydrological models in regions of complex terrain. Global Planetary Change 25(1–2):111–132CrossRefGoogle Scholar
  4. Bishop AW (1955) The use of slip circle in the stability analysis of earth slopes. Geotechnique 5(1):7–17Google Scholar
  5. Bishop AW, Morgenstern NR (1960) Stability coefficients for earth slopes. Geotechnique 10(4):129–147CrossRefGoogle Scholar
  6. Boots BN, Getis A (1988) Point pattern analysis. Sage Publications, Newbury Park, CAGoogle Scholar
  7. Borga M, Vizzaccaro A (1997) On the interpolation of hydrologic variables: formal equivalence of multiquadratic surface fitting and kriging. J Hydrol 195:160–171CrossRefGoogle Scholar
  8. Burrough PA, McDonnell RA (1998) Principles of geographical information systems, Oxford University Press, OxfordGoogle Scholar
  9. Casagrande A (1936) Characteristics of cohesionless soils affecting the stability of slopes and earth fills. J Boston Soc Civil Eng 23(1):13– 32Google Scholar
  10. Cheng CZ (2003) Rainfall and geological conditions for debris flow occurrence. Master thesis of National Cheng-Kung University (in Chinese)Google Scholar
  11. Cheng JD, Huang YC, Wu HL, Yeh JL, Chang CH (2005) Hydrometeorological and landuse attributes of debris flows and debris floods during typhoon Toraji, July 29–30, 2001 in central Taiwan. J Hydrol 306(1–4):161–173CrossRefGoogle Scholar
  12. Chiu LS, Liu Z, Vongsaard J, Morain S, Budge A, Bales C, Neville P (2006a) Comparison of TRMM and water district rain rates over New Mexico. Adv Atmospheric Sci 23(1):1–13CrossRefGoogle Scholar
  13. Chiu L, Shin D-B, Kwiatkowski J (2006b) Surface rain rates from TRMM satellite algorithms. In: Qu SJ et al (eds) Earth system science remote. Springer-Tsinghua University PressGoogle Scholar
  14. Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol Model 90:69–78CrossRefGoogle Scholar
  15. Clerici A, Perego S, Telllini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48(4):349–364CrossRefGoogle Scholar
  16. Daly C, Neilson RP, Philips DL (1994) A statistical topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158CrossRefGoogle Scholar
  17. Dingman LS (2002) Physical hydrology, 2nd edn. Prentice HallGoogle Scholar
  18. Dirks KN, Hay JE, Stow CD, Harris D (1998) High-resolution studies of rainfall on Norfolk Island Part II: Interpolation of rainfall data. J Hydrol 208(1998):187–193CrossRefGoogle Scholar
  19. Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66:327–343CrossRefGoogle Scholar
  20. Gabet EJ, Mudd SM (2005) The mobilization of debris flows from shallow landslides, Geomorphology, Article in processGoogle Scholar
  21. Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 141:329–339CrossRefGoogle Scholar
  22. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129CrossRefGoogle Scholar
  23. Gómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2, 5):11–27CrossRefGoogle Scholar
  24. Hemmateenejad B, Safarpour MA (2005) Net analyte signal–artificial neuralnetwork (NAS–ANN) model for efficient nonlinear multivariate calibration. Anal Chimica Acta 535:275–285CrossRefGoogle Scholar
  25. Hevesi JA, Flint AL, Istok JD (1992) Precipitation estimation in mountainous terrain using multivariant geostatistics. Part II: isohyetal maps. J Appl Meteorol 31:677–688CrossRefGoogle Scholar
  26. Hsu K, Gao H, Soroshian S, Gupta H (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190CrossRefGoogle Scholar
  27. Hsu ML, Lu KK (2004) The geomorphic characteristics of debris flows in the 99 peaks area, nantou. J Geograph Sci 38: 1–16 (in Chinese)Google Scholar
  28. Huang TH (2002) Characteristics of rains triggering debris flows in the watershed of chenyoulen stream. Master Thesis of National Cheng-Kung University (in Chinese)Google Scholar
  29. Hürlimann M, Copons R, Altimir J (2006), Detailed debris flow hazard assessment in Andorra: a multidisciplinary approach. Geomorphology 78(3–4):359–372CrossRefGoogle Scholar
  30. International Federation of the Red Cross and Red Crescent Societies I (2001) World disasters report 2001Google Scholar
  31. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  32. Jain A, Srinivasulu S (2006) Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. J Hydrol 317(3–4, 20):291—306CrossRefGoogle Scholar
  33. Jan CD (2000) Introduction to debris flow. Technology Press (in Chinese)Google Scholar
  34. Jakob M, Weatherly H (2003) A hydroclimatic threshold for landslide initiation on the North Shore Mountains of Vancouver, British Columbia. Geomorphology 54(3–4):137–156CrossRefGoogle Scholar
  35. Jakob M (2000) The impacts of logging on landslide activity at Clayoquot Sound, British Columbia. CATENA 38(4):279–300CrossRefGoogle Scholar
  36. Jiang YC (1998) A study on the judgment of potential debris flows. Master Thesis of National Taiwan University (in Chinese)Google Scholar
  37. Johnson AM (1965) A model for debris flow. Pennsylvania State University unpublished Ph.D. dissertationGoogle Scholar
  38. Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28CrossRefGoogle Scholar
  39. Kummerow C, coauthors (2000) The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J Appl Meteor 39:1965–1982Google Scholar
  40. Kyriakidis PC, Kim J, Miller NL (2001) Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. J Appl Meteorol 40:1855–1877CrossRefGoogle Scholar
  41. Lebel T, Bastin G, Obled C, Creutin JD (1987) On the accuracy of areal rainfall estimation: a case study. Water Res Res 23(11): 2123–2138CrossRefGoogle Scholar
  42. Leflaive J, Céréghino R, Danger M, Lacroix G, Loïc TH (2005) Assessment of self-organizing maps to analyze sole-carbon source utilization profiles. J Microbiol Method 62(1):89–102CrossRefGoogle Scholar
  43. Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neural networks. Ecol Model 92:101–108CrossRefGoogle Scholar
  44. Lin ML, Jeng FS (2000) Characteristics of hazards induced by extremely heavy rainfall in Central Taiwan—Typhoon Herb. Eng Geol 58:191–207CrossRefGoogle Scholar
  45. Lin PS, Lin JY, Hung JH, Yang MD (2002) Assessing debris-flow hazard in a watershed in Taiwan. Eng Geol 66:295–313CrossRefGoogle Scholar
  46. Lin CW, Shieh CL, Yuan BD, Shieh YC, Liua SH, Lee SY (2003) Impact of Chi-Chi earthquake on the occurrence of landslides and debris flows: example from the Chenyulan River watershed, Nantou, Taiwan. Eng Geol 71:49–61CrossRefGoogle Scholar
  47. Liu YH (2001) The study of spatial characteristics on debris flow in chenyulan creek watershed. Master thesis of National Taiwan University (In Chinese)Google Scholar
  48. Longley PA, Goodchild MT, Maguire DJ, Rhind DW (2001) Geographical information systems and science. Wiley, New YorkGoogle Scholar
  49. Luino F (2005) Sequence of instability processes triggered by heavy rainfall in the northern Italy. Geomorphology 66(1–4):13–39CrossRefGoogle Scholar
  50. Luk KC, Ball JE, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33(6–7):683–693CrossRefGoogle Scholar
  51. Malet JP, Laigle D, Remaître A, Maquaire O (2005) Triggering conditions and mobility of debris flows associated to complex earthflows. Geomorphology 66(1–4): 215–235CrossRefGoogle Scholar
  52. Metternicht G, Hurni L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98(2–3):284–303Google Scholar
  53. Navone HD, Ceccatto HA (1994) Predicting Indian monsoon rainfall: a neural network approach. Clim Dynam 10: 305–312CrossRefGoogle Scholar
  54. Pérez-Magariño S, Ortega-Heras M, González-San José ML, Boger Z (2004) Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines. Talanta 62(5, 19):983–990CrossRefGoogle Scholar
  55. Philips DL, Dolph J, Marks D (1992) A comparison of geostatistical procedures for spatial analysis of precipitations in mountainous terrain. Agr Forest Meteor 58:119–141CrossRefGoogle Scholar
  56. Prudhomme C (1999) Mapping a statistic of extreme rainfall in a mountainous region, Physics and Chemistry of the Earth, Part B: Hydrology. Oceans Atmosphere 24(1–2):79–84Google Scholar
  57. Ramirez MC, Velho HF, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the São Paulo region. J Hydrol 301(1–4):146–162Google Scholar
  58. Shakoor A, Smithmyer AJ (2005) An analysis of storm-induced landslides in colluvial soils overlying mudrock sequences, southeastern Ohio, USA. Eng Geol 78(3–4):257–274CrossRefGoogle Scholar
  59. Sidle RC, Pearce AJ, O’Loughlin CL (1985) Hillslope stability and land use. Water Resources Monograph 11. Ameican Geophysical Union, 140Google Scholar
  60. Tabios GQ, Salas JD (1985) A comparative analysis of techniques for spatial interpolation of precipitation. Water Res Bull 21(3):365–380Google Scholar
  61. Taylor DW (1937) Stability of earth slopes. J Boston Soc Civil Eng 24:197–246Google Scholar
  62. Thiessen AH (1911) Precipitation averages for large areas. Monthly Weather Rev 39(7):1082–1084Google Scholar
  63. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geography 46(Supplement):234–240CrossRefGoogle Scholar
  64. Tseng YC (2004) A composite warning index of rainfall and geological conditions for debris flow. Master thesis of National Cheng-Kung University (in Chinese)Google Scholar
  65. Vanacker V, Vanderschaeghe M, Govers G, Willems E, Poesen J, Deckers J, De Bievre B (2003) Linking hydrological, infinite slope stability and land-use change models through GIS for assessing the impact of deforestation on slope stability in high Andean watersheds. Geomorphology 52(3–4, 16):299–315Google Scholar
  66. Wen BP, Aydin A (2005) Mechanism of a rainfall-induced slide-debris flow: constraints from microstructure of its slip zone. Eng Geol 78(1–2): 69–88CrossRefGoogle Scholar
  67. Wong DW, Lee J (2005) Statistical analysis of geographic information with ArcView GIS and ArcGIS. John Wiley & Sons, Inc, Hoboken, NJGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of Earth Systems and GeoInformation Sciences, College of ScienceGeorge Mason UniversityFairfaxUSA
  2. 2.NASA/Goddard Space Flight CenterData and Information Services CenterGreenbeltUSA

Personalised recommendations