Slope stability prediction based on IPOARF algorithm: A case study of Lala Copper Mine, Sichuan, China
This paper proposes an intelligent slope stability prediction method based on the improved pelican optimization algorithm (IPOA) and the optimization random forest (RF) algorithm to reduce disasters and accidents caused by slope instability. First, exploratory data analysis (EDA) is performed using...
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| Veröffentlicht in: | Expert systems with applications Jg. 229; S. 120595 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.11.2023
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | This paper proposes an intelligent slope stability prediction method based on the improved pelican optimization algorithm (IPOA) and the optimization random forest (RF) algorithm to reduce disasters and accidents caused by slope instability. First, exploratory data analysis (EDA) is performed using correlation diagrams, heat maps under different states, box plots, histograms, and quantile–quantile (Q-Q) diagrams of variables, followed by establishing a high-quality data set for slope engineering cases and an index system for slope stability prediction. Second, 10 benchmark functions reveal that the IPOA algorithm outperforms other algorithms. Accordingly, this paper develops a slope stability prediction model based on the IPOARF algorithm. Afterward, a set of intelligent slope stability prediction systems is created using MATLAB tools and applied to Lala Copper Mine in Sichuan Province. Finally, this paper compares the accuracy of various models and subjects the proposed model to additional testing. The results reveal that the prediction model based on improved IPOA and RF algorithms is reliable and effective, with an accuracy of up to 90.4%, which can serve as a solid technical basis for slope instability disaster prediction in geotechnical engineering. |
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| AbstractList | This paper proposes an intelligent slope stability prediction method based on the improved pelican optimization algorithm (IPOA) and the optimization random forest (RF) algorithm to reduce disasters and accidents caused by slope instability. First, exploratory data analysis (EDA) is performed using correlation diagrams, heat maps under different states, box plots, histograms, and quantile–quantile (Q-Q) diagrams of variables, followed by establishing a high-quality data set for slope engineering cases and an index system for slope stability prediction. Second, 10 benchmark functions reveal that the IPOA algorithm outperforms other algorithms. Accordingly, this paper develops a slope stability prediction model based on the IPOARF algorithm. Afterward, a set of intelligent slope stability prediction systems is created using MATLAB tools and applied to Lala Copper Mine in Sichuan Province. Finally, this paper compares the accuracy of various models and subjects the proposed model to additional testing. The results reveal that the prediction model based on improved IPOA and RF algorithms is reliable and effective, with an accuracy of up to 90.4%, which can serve as a solid technical basis for slope instability disaster prediction in geotechnical engineering. |
| ArticleNumber | 120595 |
| Author | Li, Mingliang Qin, Qingci Li, Kegang Yue, Rui |
| Author_xml | – sequence: 1 givenname: Mingliang surname: Li fullname: Li, Mingliang email: 962864321@qq.com organization: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Yunnan 650093, China – sequence: 2 givenname: Kegang orcidid: 0000-0002-6961-7258 surname: Li fullname: Li, Kegang email: likegang_78@163.com organization: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Yunnan 650093, China – sequence: 3 givenname: Qingci surname: Qin fullname: Qin, Qingci email: qinqingci@stu.kust.edu.cn organization: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Yunnan 650093, China – sequence: 4 givenname: Rui surname: Yue fullname: Yue, Rui email: yr@stu.kust.edu.cn organization: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Yunnan 650093, China |
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| Title | Slope stability prediction based on IPOARF algorithm: A case study of Lala Copper Mine, Sichuan, China |
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