This study proposes a novel ensemble method based on weighted majority voting to evaluate the slope stability. The ensemble classifier is composed of 5 base classifiers, including random forest, logistic regression, naive bayes, support vector classifier and back propagation. An integrated approach was developed using 213 slope cases collected from the literature and the performance of the proposed approach was validated. The selection of training parameters was carried out by the definition of safety factor and the correlation analysis of parameters. The search for the optimal hyperparameters of the base classifiers is performed using a grid search algorithm combined with a five-fold cross-validation. Weights to each base classifier is obtained by the AUC (area under the curve) value of the training dataset. Finally, the ensemble method based on weights is established to predict the stability of slopes in this paper. It is demonstrated that the ensemble algorithm is superior to the base classifier with regard to accuracy, kappa, precision, recall, F1 score and the receiver's operating characteristic curve AUC. Also, the importance scores of training parameters are obtained by the random forest theory.

本研究提出了一种基于加权多数投票的新型集成方法来评估边坡稳定性。集成分类器由5个基分类器组成，包括随机森林、逻辑回归、朴素贝叶斯、支持向量分类器和反向传播。使用从文献中收集的 213 个斜坡案例开发了一种综合方法，并验证了所提出方法的性能。通过安全系数的定义和参数的相关性分析进行训练参数的选择。使用网格搜索算法结合五重交叉验证来搜索基分类器的最佳超参数。每个基分类器的权重是通过训练数据集的 AUC（曲线下面积）值获得的。最后，本文建立了基于权重的集成方法来预测边坡的稳定性。结果表明，集成算法在准确率、kappa、精确率、召回率、F1 分数和接收者操作特征曲线 AUC 方面均优于基分类器。此外，训练参数的重要性得分是通过随机森林理论获得的。