Abstract
This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.
Highlights
Landslide susceptibility is the likelihood of a landslide occurring in a certain area given the local terrain attributes [1]
The Landslide susceptibility maps (LSMs) produced by the decision tree (DT), random forest (RF), and rotation forest (RoF) models were evaluated by statistical indices and area under the ROC curve (AUROC)
Landslide susceptibility analyses and mapping were performed with these 11 landslide conditioning factors using DT, RF, and RoF models
Summary
Landslide susceptibility is the likelihood of a landslide occurring in a certain area given the local terrain attributes [1]. It is usually assumed that landslides will occur in the future if the conditions are the same as those that produced them in the past [2]. A landslide susceptibility map (LSM) portraying the spatial distribution of landslide susceptibility can be a useful tool for decision-makers in developing effective hazard mitigation and land-use planning. A number of different techniques have been developed and applied to produce LSMs, including heuristic, deterministic (engineering approach), and probabilistic (non-deterministic or data-driven) methods [4,5], among which probabilistic methods are generally the most widely used [5,6,7,8]. Probabilistic methods, as known as statistical methods, are based on statistical correlations between historical records of landslide occurrences and a set of influencing parameters
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