Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Navid Kardani; Annan Zhou; Majidreza Nazem; Shui-Long Shen

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Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

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Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.

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《岩石力学与岩土工程学报：英文版》 |2021年第1期|188-201|共14页
作者
Navid Kardani; Annan Zhou; Majidreza Nazem; Shui-Long Shen;
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作者单位

Civil and Infrastructure Discipline;

School of Engineering;

Royal Melbourne Institute of Technology(RMIT);

Melbourne;

Victoria;

Australia;

Department of Civil and Environmental Engineering;

College of Engineering;

Shantou University;

Shantou;

515063;

China;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
Slope stability; Machine learning(ML); Stacking ensemble; Variable importance; Artificial bee colony(ABC);

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

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