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

Abstract

Ensemble machine learning methods have been widely used for modeling landslide susceptibility, but there has been no uniform ensemble method for this problem. The main objective of this study is to compare popular ensemble machine learning-based models and apply them to landslides susceptibility mapping. The selected models include the random forest (RF), which is a typical bagging ensemble model, and three advanced boosting models, namely, adaptive boosting (AB), gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost). This study considers 94 landslide points and 12 affecting factors. The data are divided into a training dataset consisting of 70% of the overall data, and a validation dataset, containing the remaining 30% of the data. The models are evaluated using the area under the receiver operating characteristic curve (AUC) and three common performance metrics: sensitivity, specificity, and accuracy. The results indicate that the four ensemble models have an AUC of more than 0.8, suggesting that they can appropriately and accurately predict landslide susceptibility maps. In particular, the XGBoost model achieves the best performance among all models, having a sensitivity of 92.86, specificity of 90.00, and accuracy of 91.38. Furthermore, the bagging model has a sensitivity of 89.29, specificity of 86.67, and accuracy of 87.93, and it is superior to the GBDT, which achieves a sensitivity of 86.21, specificity of 86.21, and accuracy of 86.21, and the AB, reaching a sensitivity of 82.14, specificity of 80.00, and accuracy of 81.03. The results presented in this study indicate that the advanced ensemble model, the XGBoost model, could be a promising tool for the selection of ensemble models for predicting landslide susceptibility mapping.

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

“…The quantitative results show that the order of the AUC values from small to large is RF model > XGBoost model > gcForest model. Additionally, the findings agree with those of Wei et al, who reported that the prediction accuracy of an XGBoost model was higher than that of an RF model [ 32 ]. The accuracy, AUC value, recall rate, test set precision, and kappa coefficient of the gcForest model with stacking were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which are significantly better than the values of the other two models.…”

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

“…Conditioning factors selection is a fundamental step for an adequate LSM generation. In this research, the selection of factors was based mainly on the availability of information in the study area, and it is important to point out that there is no selection standard, since each study area has different characteristics [42]. In this sense, information on 15 landslide conditioning factors was available: topographic (10): elevation, slope, slope aspect, curvature, stream power index (SPI), sediment transport index (STI), topographic position index (TPI), terrain ruggedness index (TRI), topographic wetness index (TWI), and solar radiation index; land cover (3): land cover, distance to roads, and normalized difference vegetation index (NDVI); hydrological (1): distance to rivers; and geological (1): lithology.…”

“…Kavzoglu and Teke [6] analyzed the performance of different ensemble ML predictive algorithms in Macka County (Turkey) and confirmed the superiority of NGBoost. Wei et al [42] carried out a comparative study of tree based ensemble models for LSM in Laiyuan County (China) and found that XGBoost and RF obtained the best performance. Badola et al [43] conducted a landslide susceptibility analysis using XGBoost in the Chamoli district (India) and obtained a satisfactory performance.…”

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

“…The occurrence of a highway landslide disaster is closely related to topography, geological structure, hydrometeorology and human activities, and selecting suitable evaluation factors is the most important part of a landslide susceptibility evaluation, but due to the complexity of the influencing factors, there is no unified selection standard yet [27,28]. A highway, as a typical line-fitting engineering structure, has to traverse different topographic and geomorphological units, especially in mountainous areas, often spreading over slopes, valleys and mountain ranges.…”