A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of Slope Stability Using Ensemble Learning Techniques
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This chapter 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.
Prediction of Slope Stability Using Ensemble Learning Techniques
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This chapter 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.
Prediction of Slope Stability Using Ensemble Learning Techniques
Wengang, Zhang (author) / Hanlong, Liu (author) / Lin, Wang (author) / Xing, Zhu (author) / Yanmei, Zhang (author)
Application of Machine Learning in Slope Stability Assessment ; Chapter: 4 ; 45-60
2023-07-09
16 pages
Article/Chapter (Book)
Electronic Resource
English
Rock Slope Stability Prediction: A Review of Machine Learning Techniques
| Springer Verlag | 2025
Rock Slope Stability Prediction: A Review of Machine Learning Techniques
| Springer Verlag | 2025