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

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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.

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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.

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Authors and Affiliations

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

    No-Wook Park

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  1. No-Wook Park
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Correspondence to No-Wook Park.

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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

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

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