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Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

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Abstract

Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).

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Acknowledgments

This work was researched by the supporting project to educate GIS experts. Thanks are also extended to two anonymous reviewers who suggested some improvements to the manuscript.

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Authors and Affiliations

  1. Department of Spatial Information Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 608-737, Republic of Korea

    Soyoung Park, Chuluong Choi & Byungwoo Kim

  2. ZEN21, 2nd Floor, RNC Building, 981-1 Bangbae 3-dong, Seocho-gu, Seoul, 137-848, Republic of Korea

    Jinsoo Kim

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Park, S., Choi, C., Kim, B. et al. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68, 1443–1464 (2013). https://doi.org/10.1007/s12665-012-1842-5

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