Skip to main content

Advertisement

SpringerLink
Log in

Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

GIS-based spatial data integration tasks for predictive geological applications, such as landslide susceptibility analysis, have been regarded as one of the primary geological application issues of GIS. An efficient framework for proper representation and integration is required for this kind of application. This paper presents a data integration framework based on the Dempster-Shafer theory of evidence for landslide susceptibility mapping with multiple geospatial data. A data-driven information representation approach based on spatial association between known landslide occurrences and input geospatial data layers is used to assign mass functions. After defining mass functions for multiple geospatial data layers, Dempster’s rule of combination is applied to obtain a series of combined mass functions. Landslide susceptibility mapping using multiple geospatial data sets from Jangheung in Korea was conducted to illustrate the application of this methodology. The results of the case study indicated that the proposed methodology efficiently represented and integrated multiple data sets and showed better prediction capability than that of a traditional logistic regression model.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • An P, Moon WM, Bonham-Carter GF (1994) Uncertainty management in integration of exploration data using the belief function. Nonrenew Resour 3:60–71

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon, New York

    Google Scholar 

  • Carranza EJM, Hale M (2003) Evidential belief functions for geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22:117–132

    Article  Google Scholar 

  • Carranza EJM, Woldai T, Chikambwe EM (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Nat Resour Res 14:14–63

    Article  Google Scholar 

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sensing 65:1389–1399

    Google Scholar 

  • Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Dai FC, Lee CF, NG KC, Tham LG, Shun WL (2004) Logistic regression modeling of storm-induced shallow landsliding in time and space on natural terrain of Lantau Island, Hong Kong. Bull Eng Geol Environ 63:315–327

    Article  Google Scholar 

  • Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 28:325–339

    Article  Google Scholar 

  • Ercanoglue M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Greco R, Sorriso-Valvo M, Catalano E (2007) Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy. Eng Geol 89:47–66

    Article  Google Scholar 

  • Kim KS (2001) Prediction of landslide probability by geomorphic characteristics and soil properties. KIGAM Bulletin 5:29–41

    Google Scholar 

  • Le Hégarat-Mascle S, Bloch I, Vidal-Madjar D (1999) Applicatino of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35:1018–1031

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S (2007) Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea. Int J Remote Sens 28:4763–4783

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Env Geol 40:1095–1113

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Env Geol 50:847–855

    Article  Google Scholar 

  • Lee T, Richards JA, Swain PH (1987) Probabilistic and evidential approaches for multisource data analysis. IEEE Trans Geosci Remote Sens 25:283–293

    Article  Google Scholar 

  • Lee BJ, Kim YB, Lee SR, Kim JC, Kang PJ, Choi HI, Jin MS (1999) Explanatory note of the Seoul-Namchonjeom sheet (1:250, 000). Korea Institute of Geology, Mining and Materials, Daejeon

    Google Scholar 

  • Luzi L, Floriana P (1996) Application of statistical and GIS techniques for slope instability zonation (1:50, 000 Fabriano geological map sheet). Soil Dyn Earthq Eng 15:83–94

    Article  Google Scholar 

  • Moon WM (1990) Integration of geophysical and geological data using evidential belief function. IEEE Trans Geosci Remote Sens 28:711–720

    Article  Google Scholar 

  • Moon WM (1993) On mathematical representation and integration of multiple spatial geoscience data sets. Can J Remote Sens 19:251–255

    Google Scholar 

  • Park NW, Chi KH (2008) Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. Int J Remote Sens 29:247–264

    Article  Google Scholar 

  • Park NW, Chi KH, Chung CF, Kwon BD (2003) Application of spatial data integration based on the likelihood ratio function and Bayesian rule for landslide hazard mapping. J Korean Earth Sci Soc 24:428–439

    Google Scholar 

  • Rottensteiner F, Trinder J, Clode S, Kubik K (2004) Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection. Inf Fusion 6:283–300

    Article  Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    Google Scholar 

  • Shi G (1994) Evidential reasoning for geological mapping with multisource spatial data. Master thesis, The University of Calgary

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorpholoy 101:572–582

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by INHA UNIVERSITY Research Grant (INHA-40227). The author would like to thank Dr. C.F. Chung of Geological Survey of Canada for his comments on validation for predictive spatial data integration.

Author information

Authors and Affiliations

  1. Department of Geoinformatic Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon, 402-751, Korea

    No-Wook Park

Authors
  1. No-Wook Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to No-Wook Park.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, NW. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62, 367–376 (2011). https://doi.org/10.1007/s12665-010-0531-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-010-0531-5

Keywords

Search

Navigation