Abstract
While multi-year and event-based landslide inventories are both commonly used in landslide susceptibility analysis, most areas lack multi-year landslide inventories, and the analysis results obtained from the use of event-based landslide inventories are very sensitive to the choice of event. Based on 24 event-based landslide inventories for the Shihmen watershed from 1996 to 2015, this study established five event-based single landslide susceptibility models employing logistic regression, random forest, support vector machine, kernel logistic regression, and gradient boosting decision tree methods. The ensemble methods, involving calculating the mean of the susceptibility indexes (PM), median of the susceptibility indexes (PME), weighted mean of the susceptibility indexes (PMW), and committee average of binary susceptibility values (CA) of the five single models were then used to establish four event-based ensemble landslide susceptibility models. After establishing nine landslide susceptibility models, using each inventory from the 24 event-based landslide inventories or a multi-year landslide inventory, we identified the differences in landslide susceptibility maps attributable to the different landslide inventories and modeling methods, and used the area under the receiver operating characteristic curve to assess the accuracy of the models. The results indicated that an ensemble model based on a multi-year inventory can obtain excellent predictive accuracy. The predictive accuracy of multi-year landslide susceptibility models is found to be superior to that of event-based models. In addition, the higher predictive accuracy of ensemble landslide susceptibility models than that of single models implied that these ensemble methods were robust for enhancing the model’s predictive performance in the study area. When employing event-based landslide inventories in modeling, PM ensemble models offer the best predictive ability, according to the Kruskal–Wallis test results. Areas with a high mean susceptibility index and low standard deviation, identified using the 24 PM ensemble models based on different event-based landslide inventories, constitute places where landslide mitigation measures should be prioritized.
Highlights
Under the impact of climate change, extreme rainfall events have caused frequent landslides and debris flows in Taiwan’s mountainous areas
Because the goal of landslide susceptibility analysis is to predict whether landslides will occur in individual slope units, the dependent variables in this model consisted of the binary response variables of “landslide” and “no-landslide”, and the logistic regression developed by Menard [36] was used to establish a parametric machine learning model, which took the form shown in Equation (1): ln pi 1 − pi k
The average predictive accuracy of event-based committee averaging (CA) models ranged from 72.7% to 77.1%, while the mean was 74.8%; the mean predictive accuracy of the multi-year CA model was 89.0%. These results indicate that the event-based ensemble models all had an area under the ROC curve (AUROC) > 50% with regard to other landslide events (i = 1–24, j = 1–24, i = j, k = 6–9)
Summary
Under the impact of climate change, extreme rainfall events have caused frequent landslides and debris flows in Taiwan’s mountainous areas. Parametric machine learning algorithms may employ logistic regression and linear discriminant analysis, and logistic regression, in particular, is often applied in landslide susceptibility analysis [3,4,5,6,7]. For their part, nonparametric machine learning algorithms do not require prior selection of the functional form and may fit the function of any form through the training process. The nonparametric machine learning algorithms most commonly used in landslide susceptibility analysis include the support vector machine [9,10,11,12,13,14,15], random forest [8,9,12,15,16,17,18], kernel logistic regression [10,11,19], and boosted regression tree [15,16,17]
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