气候变化领域集成服务门户(CCPortal): A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan-Tibet Highway, China

CCPortal

DOI	10.1155/2023/6651323
	A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan-Tibet Highway, China
	Shu, Tao; Tao, Wei; Lu, Haotian; Li, Hao; Cao, Jingxuan; Ma, Junwei
发表日期	2023
ISSN	1687-8086
EISSN	1687-8094
卷号	2023
英文摘要	Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF-PSO-LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.
英文关键词	TIME-SERIES ANALYSIS; 3 GORGES RESERVOIR; LANDSLIDE DISPLACEMENT
WOS研究方向	Construction & Building Technology ; Engineering, Civil
WOS记录号	WOS:001070565000003
来源期刊	ADVANCES IN CIVIL ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283368
作者单位	Ocean University of China; Ocean University of China; Ocean University of China; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; Institute of Mountain Hazards & Environment, CAS
推荐引用方式 GB/T 7714	Shu, Tao,Tao, Wei,Lu, Haotian,et al. A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan-Tibet Highway, China[J],2023,2023.
APA	Shu, Tao,Tao, Wei,Lu, Haotian,Li, Hao,Cao, Jingxuan,&Ma, Junwei.(2023).A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan-Tibet Highway, China.ADVANCES IN CIVIL ENGINEERING,2023.
MLA	Shu, Tao,et al."A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan-Tibet Highway, China".ADVANCES IN CIVIL ENGINEERING 2023(2023).