An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district View Full Text


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Article Info

DATE

2014-11-09

AUTHORS

Taskin Kavzoglu, Emrehan Kutlug Sahin, Ismail Colkesen

ABSTRACT

Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been suggested and applied in the literature, which are generally categorized as bivariate and multivariate. Here, in this paper, the most popular and widely used multivariate [support vector regression (SVR), logistic regression (LR) and decision tree (DT)] and bivariate methods [frequency ratio (FR), weight of evidence (WOE) and statistical index (SI)] were compared with respect to their performances in landslide susceptibility modeling problem. Duzkoy district of Trabzon Province was selected due to its unique topographical and lithological characteristics, magnifying shallow landslide risk potential. Slope, lithology, land cover, aspect, normalized difference vegetation index, soil thickness, drainage density, topographical wetness index and elevation were employed as landslide occurrence factors. Accuracy measures based on confusion matrix (i.e., overall accuracy and Kappa coefficient) and receiver operating characteristic (ROC) curve were employed to compare the performances of the methods. Furthermore, McNemar’s test was employed to analyze the statistical significance of differences in method performances. The results indicated that multivariate approaches (i.e., SVR, LR and DT) outperformed the bivariate methods (i.e., FR, SI and WOE) by about 13 %. Within the multivariate approaches, SVR method performed the best with the highest accuracy, while FR method was the most effective and accurate bivariate method. Interpretation of AUC values and the McNemar’s statistical test results revealed that the SVR method was superior in modeling landslide susceptibility compared with the other multivariate and bivariate methods. More... »

PAGES

471-496

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    33 schema:description Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been suggested and applied in the literature, which are generally categorized as bivariate and multivariate. Here, in this paper, the most popular and widely used multivariate [support vector regression (SVR), logistic regression (LR) and decision tree (DT)] and bivariate methods [frequency ratio (FR), weight of evidence (WOE) and statistical index (SI)] were compared with respect to their performances in landslide susceptibility modeling problem. Duzkoy district of Trabzon Province was selected due to its unique topographical and lithological characteristics, magnifying shallow landslide risk potential. Slope, lithology, land cover, aspect, normalized difference vegetation index, soil thickness, drainage density, topographical wetness index and elevation were employed as landslide occurrence factors. Accuracy measures based on confusion matrix (i.e., overall accuracy and Kappa coefficient) and receiver operating characteristic (ROC) curve were employed to compare the performances of the methods. Furthermore, McNemar’s test was employed to analyze the statistical significance of differences in method performances. The results indicated that multivariate approaches (i.e., SVR, LR and DT) outperformed the bivariate methods (i.e., FR, SI and WOE) by about 13 %. Within the multivariate approaches, SVR method performed the best with the highest accuracy, while FR method was the most effective and accurate bivariate method. Interpretation of AUC values and the McNemar’s statistical test results revealed that the SVR method was superior in modeling landslide susceptibility compared with the other multivariate and bivariate methods.
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    41 Duzkoy district
    42 FR method
    43 McNemar test
    44 McNemar’s statistical test results
    45 Province
    46 SVR method
    47 Trabzon province
    48 accuracy
    49 accuracy measures
    50 accurate bivariate method
    51 approach
    52 area
    53 aspects
    54 assessment
    55 assessment of multivariate
    56 authorities
    57 bivariate
    58 bivariate approach
    59 bivariate methods
    60 case study
    61 characteristic curve
    62 characteristics
    63 confusion matrix
    64 cover
    65 curves
    66 density
    67 difference vegetation index
    68 differences
    69 disaster mitigation works
    70 district
    71 drainage density
    72 elevation
    73 factors
    74 future investments
    75 hazard-prone areas
    76 high accuracy
    77 index
    78 interpretation
    79 investment
    80 land cover
    81 landslide occurrence factors
    82 landslide risk potential
    83 landslide susceptibility
    84 landslide susceptibility assessment
    85 landslide susceptibility map
    86 landslide susceptibility mapping
    87 landslide susceptibility modeling problem
    88 limitations
    89 literature
    90 lithological characteristics
    91 lithology
    92 local authorities
    93 mapping
    94 maps
    95 matrix
    96 measures
    97 method
    98 method performance
    99 mitigation works
    100 modeling problem
    101 multivariate
    102 multivariate approach
    103 normalized difference vegetation index
    104 occurrence factors
    105 paper
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    107 potential
    108 problem
    109 purpose
    110 receiver
    111 respect
    112 results
    113 risk potential
    114 shallow landslide risk potential
    115 significance
    116 slope
    117 soil thickness
    118 source
    119 statistical significance
    120 statistical test results
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