Cai Yuan, Yuan Ying, Zhou Aihong
School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, 050031, China.
Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang, 050031, China.
Sci Rep. 2024 Oct 28;14(1):25727. doi: 10.1038/s41598-024-77058-6.
A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning.
利用分类提升（CatBoost）开发了一种预测边坡稳定性的模型，该模型纳入了6个边坡特征来表征边坡稳定性状态。该模型以对称树作为基础模型进行训练，利用有序提升来替代梯度估计，从而提高预测精度。还引入了包括支持向量机（SVM）、轻量级梯度提升机（LGBM）、随机森林（RF）和逻辑回归（LR）在内的对比模型。使用五个性能评估指标来评估CatBoost模型的预测能力。基于CatBoost模型，计算边坡失稳的预测概率，并进一步建立边坡失稳预警模型。结果表明，CatBoost模型在训练集和测试集之间的准确率差异为6.25%，精度达到100%，曲线下面积（AUC）值为0.95。这表明该模型具有较高的预测精度和强大的排序能力，有效缓解了过拟合问题。边坡失稳预警模型对预警级别提供了合理的分类，为边坡稳定性预测和失稳预警的研究及实际应用提供了有价值的见解。
