Skip to main content

Advertisement

Log in

Vulnerability assessment of rainfall-induced debris flows in Taiwan

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • 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 124:85–95

    Article  Google Scholar 

  • 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):15–31

    Article  Google Scholar 

  • 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):111–132

    Article  Google Scholar 

  • Bishop AW (1955) The use of slip circle in the stability analysis of earth slopes. Geotechnique 5(1):7–17

    Google Scholar 

  • Bishop AW, Morgenstern NR (1960) Stability coefficients for earth slopes. Geotechnique 10(4):129–147

    Article  Google Scholar 

  • Boots BN, Getis A (1988) Point pattern analysis. Sage Publications, Newbury Park, CA

    Google Scholar 

  • Borga M, Vizzaccaro A (1997) On the interpolation of hydrologic variables: formal equivalence of multiquadratic surface fitting and kriging. J Hydrol 195:160–171

    Article  Google Scholar 

  • Burrough PA, McDonnell RA (1998) Principles of geographical information systems, Oxford University Press, Oxford

  • Casagrande A (1936) Characteristics of cohesionless soils affecting the stability of slopes and earth fills. J Boston Soc Civil Eng 23(1):13– 32

    Google Scholar 

  • Cheng CZ (2003) Rainfall and geological conditions for debris flow occurrence. Master thesis of National Cheng-Kung University (in Chinese)

  • 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):161–173

    Article  Google Scholar 

  • 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):1–13

    Article  Google Scholar 

  • 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 Press

  • Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol Model 90:69–78

    Article  Google Scholar 

  • Clerici A, Perego S, Telllini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48(4):349–364

    Article  Google Scholar 

  • Daly C, Neilson RP, Philips DL (1994) A statistical topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158

    Article  Google Scholar 

  • Dingman LS (2002) Physical hydrology, 2nd edn. Prentice Hall

  • 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):187–193

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

    Article  Google Scholar 

  • Gabet EJ, Mudd SM (2005) The mobilization of debris flows from shallow landslides, Geomorphology, Article in process

  • Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 141:329–339

    Article  Google Scholar 

  • Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129

    Article  Google Scholar 

  • 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):11–27

    Article  Google Scholar 

  • Hemmateenejad B, Safarpour MA (2005) Net analyte signal–artificial neuralnetwork (NAS–ANN) model for efficient nonlinear multivariate calibration. Anal Chimica Acta 535:275–285

    Article  Google Scholar 

  • Hevesi JA, Flint AL, Istok JD (1992) Precipitation estimation in mountainous terrain using multivariant geostatistics. Part II: isohyetal maps. J Appl Meteorol 31:677–688

    Article  Google Scholar 

  • Hsu K, Gao H, Soroshian S, Gupta H (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190

    Article  Google Scholar 

  • 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 

  • Huang TH (2002) Characteristics of rains triggering debris flows in the watershed of chenyoulen stream. Master Thesis of National Cheng-Kung University (in Chinese)

  • Hürlimann M, Copons R, Altimir J (2006), Detailed debris flow hazard assessment in Andorra: a multidisciplinary approach. Geomorphology 78(3–4):359–372

    Article  Google Scholar 

  • International Federation of the Red Cross and Red Crescent Societies I (2001) World disasters report 2001

  • Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York

  • 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—306

    Article  Google Scholar 

  • Jan CD (2000) Introduction to debris flow. Technology Press (in Chinese)

  • Jakob M, Weatherly H (2003) A hydroclimatic threshold for landslide initiation on the North Shore Mountains of Vancouver, British Columbia. Geomorphology 54(3–4):137–156

    Article  Google Scholar 

  • Jakob M (2000) The impacts of logging on landslide activity at Clayoquot Sound, British Columbia. CATENA 38(4):279–300

    Article  Google Scholar 

  • Jiang YC (1998) A study on the judgment of potential debris flows. Master Thesis of National Taiwan University (in Chinese)

  • Johnson AM (1965) A model for debris flow. Pennsylvania State University unpublished Ph.D. dissertation

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28

    Article  Google Scholar 

  • Kummerow C, coauthors (2000) The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J Appl Meteor 39:1965–1982

    Google Scholar 

  • Kyriakidis PC, Kim J, Miller NL (2001) Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. J Appl Meteorol 40:1855–1877

    Article  Google Scholar 

  • Lebel T, Bastin G, Obled C, Creutin JD (1987) On the accuracy of areal rainfall estimation: a case study. Water Res Res 23(11): 2123–2138

    Article  Google Scholar 

  • 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):89–102

    Article  Google Scholar 

  • Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neural networks. Ecol Model 92:101–108

    Article  Google Scholar 

  • Lin ML, Jeng FS (2000) Characteristics of hazards induced by extremely heavy rainfall in Central Taiwan—Typhoon Herb. Eng Geol 58:191–207

    Article  Google Scholar 

  • Lin PS, Lin JY, Hung JH, Yang MD (2002) Assessing debris-flow hazard in a watershed in Taiwan. Eng Geol 66:295–313

    Article  Google Scholar 

  • 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 71:49–61

    Article  Google Scholar 

  • Liu YH (2001) The study of spatial characteristics on debris flow in chenyulan creek watershed. Master thesis of National Taiwan University (In Chinese)

  • Longley PA, Goodchild MT, Maguire DJ, Rhind DW (2001) Geographical information systems and science. Wiley, New York

  • Luino F (2005) Sequence of instability processes triggered by heavy rainfall in the northern Italy. Geomorphology 66(1–4):13–39

    Article  Google Scholar 

  • Luk KC, Ball JE, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33(6–7):683–693

    Article  Google Scholar 

  • Malet JP, Laigle D, Remaître A, Maquaire O (2005) Triggering conditions and mobility of debris flows associated to complex earthflows. Geomorphology 66(1–4): 215–235

    Article  Google Scholar 

  • 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):284–303

    Google Scholar 

  • Navone HD, Ceccatto HA (1994) Predicting Indian monsoon rainfall: a neural network approach. Clim Dynam 10: 305–312

    Article  Google Scholar 

  • 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):983–990

    Article  Google Scholar 

  • Philips DL, Dolph J, Marks D (1992) A comparison of geostatistical procedures for spatial analysis of precipitations in mountainous terrain. Agr Forest Meteor 58:119–141

    Article  Google Scholar 

  • 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):79–84

    Google Scholar 

  • 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):146–162

    Google Scholar 

  • 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):257–274

    Article  Google Scholar 

  • Sidle RC, Pearce AJ, O’Loughlin CL (1985) Hillslope stability and land use. Water Resources Monograph 11. Ameican Geophysical Union, 140

  • Tabios GQ, Salas JD (1985) A comparative analysis of techniques for spatial interpolation of precipitation. Water Res Bull 21(3):365–380

    Google Scholar 

  • Taylor DW (1937) Stability of earth slopes. J Boston Soc Civil Eng 24:197–246

    Google Scholar 

  • Thiessen AH (1911) Precipitation averages for large areas. Monthly Weather Rev 39(7):1082–1084

    Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geography 46(Supplement):234–240

    Article  Google Scholar 

  • Tseng YC (2004) A composite warning index of rainfall and geological conditions for debris flow. Master thesis of National Cheng-Kung University (in Chinese)

  • 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):299–315

    Google Scholar 

  • 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): 69–88

    Article  Google Scholar 

  • Wong DW, Lee J (2005) Statistical analysis of geographic information with ArcView GIS and ArcGIS. John Wiley & Sons, Inc, Hoboken, NJ

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to George Y. Lu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lu, G.Y., Chiu, L.S. & Wong, D.W. Vulnerability assessment of rainfall-induced debris flows in Taiwan. Nat Hazards 43, 223–244 (2007). https://doi.org/10.1007/s11069-006-9105-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-006-9105-y

Keywords

Navigation