基于WOA-RF的边坡稳定性预测模型

Slope Stability Prediction Based on WOA-RF Hybrid Model

为有效地预测边坡稳定性和预防边坡失稳事故的发生,提出了鲸鱼优化算法(whale optimization algorithm,WOA)和随机森林(random forest,RF)相结合的混合模型WOA-RF;基于所收集的边坡案例,采用混淆矩阵的分类性能指标和受试者工作特征曲线及线下面积评估混合模型WOA-RF的分类和泛化性能;使用WOA对4种广泛应用的机器学习模型进行优化,并将优化后的机器学习模型与WOA-RF模型进行对比分析。结果表明:WOA可以有效地优化超参数和提升模型性能;最优WOA-RF模型在训练集和测试集上的准确率分别为0.99和0.94,优化后,准确率、精确率、召回率、精确率和召回率的加权平均值分别提升了11.9%、19.0%、4.8%和11.9%;对比分析各个模型的预测性能后发现,WOA-RF模型的各项指标均优于其他模型;确定了特征重要性排序,发现容重是影响边坡稳定性的最敏感特征。WOA-RF模型可有效地预测边坡稳定性,预测结果可为防护措施的制定提供依据。

To effectively predict slope stability and prevent slope instability occurrence,a hybrid model WOA-RF,combining whale optimization algorithm(WOA)and random forest(RF)was proposed.Based on the collected slope cases,the classification and generalization performance of the model was evaluated according to the classification performance indicators given by the confusion matrix and the area under the receiver operating characteristic curve.Additionally,WOA was used to optimize four widely used machine learning models,and the optimized machine learning models were compared with WOA-RF.The results demonstrate that WOA is effective in optimizing hyperparameters and improving model performance.The optimal WOA-RF model achieves an accuracy of 0.99 on training set and of 0.94 on test set.After optimization,the accuracy,the precision,the recall,and the hamonic mean of the precision and recall are increased by 11.9%,19.0%,4.8%,and 11.9%,respectively.Comparative analysis reveals that the WOA-RF model is superior to the others in all indicators.Furthermore,the feature importance ranking was determined.Analysis of the feature importance indicates that unit weight is the most sensitive feature affecting slope stability.The established WOA-RF model is proved effective in predicting slope stability and facilitating the development of appropriate protective measures based on the predicted results.