高压物理学报2024，Vol.38Issue(3)：191-202,12.DOI:10.11858/gywlxb.20230837
基于WOA-RF的边坡稳定性预测模型
Slope Stability Prediction Based on WOA-RF Hybrid Model
摘要
Abstract
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.关键词
边坡稳定性预测/机器学习/鲸鱼优化算法/随机森林/特征重要性Key words
slope stability prediction/machine learning/whale optimization algorithm/random forest/feature importance分类
数理科学引用本文复制引用
张建涛,刘志祥,张双侠,郭腾飞,袁丛祥..基于WOA-RF的边坡稳定性预测模型[J].高压物理学报,2024,38(3):191-202,12.基金项目
国家重点研发计划项目(2022YFC2904101) （2022YFC2904101）
国家自然科学基金(52374107,51974359) （52374107,51974359）
