IMHE OpenIR  > 山地灾害与地表过程重点实验室
Application of GA-SVM method with parameter optimization for landslide development prediction
Li, X. Z.1,2; Kong, J. M.1,2
Corresponding AuthorJ. M. Kong
2014
Source PublicationNATURAL HAZARDS AND EARTH SYSTEM SCIENCES
ISSN1561-8633
Volume14Issue:3Pages:525-533
SubtypeArticle
Abstract
; Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and gamma) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.
KeywordLandslide Prediction Support Vector Machine (Svm) Genetic Algorithm (Ga) Ga-svm Parameter Optimization
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.5194/nhess-14-525-2014
URL查看原文
WOS Subject ExtendedGeology ; Meteorology & Atmospheric Sciences ; Water Resources
Indexed BySCI
WOS KeywordSUPPORT VECTOR MACHINE ; GENETIC ALGORITHM ; SLOPE STABILITY ; REGRESSION ; TUTORIAL ; NETWORKS ; FAILURE ; TIME
Language英语
Quartile3区
TOP
WOS SubjectGeosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS IDWOS:000334093600003
Funding OrganizationNational Key Basic Research Program of China(2013CB733205) ; Key Deployment Research Program of CAS(KZZD-EW-05-01-02) ; National Natural Science Foundation of China(40802072) ; Key Research Program of Key Laboratory of Mountain Hazards and Surface Process, CAS(Y3K2040040)
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Cited Times:56[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/7469
Collection山地灾害与地表过程重点实验室
Affiliation1.Chinese Acad Sci, Key Lab Mt Hazards & Surface Proc, Chengdu 610041, Peoples R China
2.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Li, X. Z.,Kong, J. M.. Application of GA-SVM method with parameter optimization for landslide development prediction[J]. NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,2014,14(3):525-533.
APA Li, X. Z.,&Kong, J. M..(2014).Application of GA-SVM method with parameter optimization for landslide development prediction.NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,14(3),525-533.
MLA Li, X. Z.,et al."Application of GA-SVM method with parameter optimization for landslide development prediction".NATURAL HAZARDS AND EARTH SYSTEM SCIENCES 14.3(2014):525-533.
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