Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction
文献类型：期刊论文
| 作者 | Lin, Shan2,3; Zheng, Hong3; Han, Bei3; Li, Yanyan3; Han, Chao3; Li, Wei1 |
| 刊名 | ACTA GEOTECHNICA
 |
| 出版日期 | 2022-01-23 |
| 页码 | 26 |
| 关键词 | Classification Ensemble learning Machine learning (ML) Repeated cross-validation Slope stability |
| ISSN号 | 1861-1125 |
| DOI | 10.1007/s11440-021-01440-1 |
| 英文摘要 | Slope engineering is a complex nonlinear system. It is difficult to respond with a high level of precision and efficiency requirements for stability assessment using conventional theoretical analysis and numerical computation. An ensemble learning algorithm for solving highly nonlinear problems is introduced in this paper to study the stability of 444 slope cases. Different ensemble learning methods [AdaBoost, gradient boosting machine (GBM), bagging, extra trees (ET), random forest (RF), hist gradient boosting, voting and stacking] for slope stability assessment are studied and compared to make the best use of the large variety of existing statistical and ensemble learning methods collected. Six potential relevant indicators, gamma, C, phi, beta, H and r(u), are chosen as the prediction indicators. The tenfold CV method is used to improve the generalization ability of the classification models. By analysing the evaluation indicators AUC, accuracy, kappa value and log loss, the stacking model shows the best performance with the highest AUC (0.9452), accuracy (84.74%), kappa value (0.6910) and lowest log loss (0.3282), followed by ET, RF, GBM and bagging models. The analysis of engineering examples shows that the ensemble learning algorithm can deal with this relationship well and give accurate and reliable prediction results, which has good applicability for slope stability evaluation. Additionally, geotechnical material variables are found to be the most influential variables for slope stability prediction. |
| 资助项目 | Open Research Fund of the State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, and Chinese Academy of Sciences[Z019008] ; Natural Science Foundation of China[42107214] ; Natural Science Foundation of China[11972043] ; Natural Science Foundation of China[11902134] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000745784000001 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/34376]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Han, Bei |
| 作者单位 | 1.Linyi Univ, Sch Civil Engn & Architecture, Linyi 276000, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Beijing Univ Technol, China Minist Educ, Key Lab Urban Secur & Disaster Engn, Beijing 100124, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lin, Shan,Zheng, Hong,Han, Bei,et al. Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction[J]. ACTA GEOTECHNICA,2022:26. |
| APA | Lin, Shan,Zheng, Hong,Han, Bei,Li, Yanyan,Han, Chao,&Li, Wei.(2022).Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction.ACTA GEOTECHNICA,26. |
| MLA | Lin, Shan,et al."Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction".ACTA GEOTECHNICA (2022):26. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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