Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest (RF) and extreme gradient boosting (XGBoost). As an illustration, the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County, Chongqing, China. For comparison, the predictive performance of RF, XGBoost, support vector machine (SVM), and logistic regression (LR) is systematically investigated based on the well-established confusion matrix, which contains the known indices of recall rate, precision, and accuracy. Furthermore, the feature importance of the…

• NN
  National Natural Science Foundation of China

  Award: 52008058
• NK
  National Key Research and Development Program of China

  Award: 2019YFC1509605
• HF
  High-end Foreign Experts Recruitment Plan of China

  Award: G20200022005