Econ. Environ. Geol. 2005; 38(1): 45-55

Published online February 28, 2005

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

Application of GIS-based Probabilistic Empirical and Parametric Models for Landslide Susceptibility Analysis

No-Wook Park1*, Kwang-Hoon Chi1, Chang-Jo F. Chung2 and Byung-Doo Kwon3

1Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea
2Geological Survey of Canada
3Department of Earth Sciences, Seoul National University, Seoul 151-742, Korea

Correspondence to :

No-Wook Park

nwpark@kigam.re.kr

Received: October 12, 2004; Accepted: January 17, 2005

Abstract

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.

Keywords spatial integration, likelihood ratio, discriminant analysis, landslide susceptibility

Article

Econ. Environ. Geol. 2005; 38(1): 45-55

Published online February 28, 2005

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

Application of GIS-based Probabilistic Empirical and Parametric Models for Landslide Susceptibility Analysis

No-Wook Park1*, Kwang-Hoon Chi1, Chang-Jo F. Chung2 and Byung-Doo Kwon3

1Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea
2Geological Survey of Canada
3Department of Earth Sciences, Seoul National University, Seoul 151-742, Korea

Correspondence to:

No-Wook Park

nwpark@kigam.re.kr

Received: October 12, 2004; Accepted: January 17, 2005

Abstract

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.

Keywords spatial integration, likelihood ratio, discriminant analysis, landslide susceptibility

    KSEEG
    Jun 30, 2024 Vol.57 No.3, pp. 281~352

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