Landslide Susceptibility Mapping by Using Landslide RatioBased Logistic Regression:A Case Study in the Southern Taiwan

The object of the research is to compare the model performance and explain the error source of original logistic regression landslide susceptibility model(abbreviated as or-LRLSM) and landslide ratio-based logistic regression landslide susceptibility model(abbreviated as lr-LRLSM) in the Chishan watershed with a serious landslide disaster after 2009 Typhoon Morakot. The landslide inventory induced by 2009 Typhoon Morakot in South Taiwan is the main research material, while the Chishan watershed is the research area. Six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The performance of lr-LRLSM is better than that of or-LRLSM. The Cox Snell R2, Nagelkerke R2 value, and the area under the relative operating characteristic curve(abbreviated as AUC) of lrLRLSM is larger than those of or-LRLSM, and the average correct ratio for the lr-LRLSM to predict landslide or non-landslide is larger than that of orLRLSM by 5.0%. The increase of the average correct ratio(abbreviated as ACR) difference from or-LRLSM to lr-LRLSM shows in slope, revised accumulated rainfall, aspect, geological formation and bank erosion variables, and only light decreases in elevation variable. The error sources of continuous variables in building the or-LRLSM is the dissimilarity between the distribution of landslide ratio and production of coefficient and characteristic values, while those of categorical variables is due to low correlation of landslide ratio and the coefficient value of each parameter. Using the classification of landslide ratio as the database to build logistic regression landslide susceptibility model(abbreviated as LRLSM) can revise the errors. The comparison of or-LRLSM and lr-LRLSM in the Chishan watershed also shows that building the landslide susceptibility model(abbreviated as LSM) by using lr-LRLSM is practical and of better performance than that by using the or-LRLSM.