Stability (learning theory)

Mathematics

Slope stability

Support vector machine

Statistics

Machine learning

Logistic regression

Engineering

Confusion matrix

Random forest

Landslide

Artificial intelligence

Geotechnical engineering

Computer science

Ensemble learning

Boosting (machine learning)

Decision tree

Journal of Rock Mechanics and Geotechnical Engineering

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 12 influencing variables is also explored. Results show that the accuracy of the XGBoost and RF for both the training and testing data is superior to that of SVM and LR, revealing the superiority of the ensemble learning models (i.e. XGBoost and RF) in the slope stability prediction of Yunyang County. Among the 12 influencing factors, the profile shape is the most important one. The proposed ensemble learning-based method offers a promising way to rationally capture the slope status. It can be extended to the prediction of slope stability of other landslide-prone areas of interest.

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