Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model

A landslide disaster, especially a highway landslide, may greatly impact the transport capacity of nearby roads. Keeping highways open, in particular, is crucial for supporting the functioning of the economy, society and people. Therefore, evaluating the highway landslide susceptibility is particularly important. In this paper, the city of Laibin, in the Guangxi Zhuang Autonomous Region of China, was taken as the study zone. According to data on 641 highway landslide disaster points measured in the field and a basic evaluation of the study area, nine evaluation factors—the elevation, slope, aspect, height difference, plan curve, profile curve, precipitation, Topographic Wetness Index (TWI) and vegetation coverage—were selected. We coupled a Frequency Ratio (FR) model, Analytic Hierarchy Process (AHP), Logistic Regression (LR), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to evaluate the susceptibility to highway landslides, with a Receiver Operating Characteristic (ROC) curve used to analyze the precision of these models. The ROC curve showed that the accuracy of the five models was greater than 0.700 and thus had a certain reliability. Among them, the FR-LR model had the highest accuracy, at 0.804. The study protocol presented here can therefore provide a reference for evaluation studies on landslide susceptibility in other areas.