Application of GA-SVM method with parameter optimization for landslide development prediction

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 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
刊名	NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
	2014
卷号	14 期号:3 页码:525-533
关键词	Landslide prediction Support vector machine (SVM) Genetic algorithm (GA) GA-SVM parameter optimization
ISSN号	1561-8633
通讯作者	J. M. Kong
中文摘要
英文摘要	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.
WOS标题词	Science & Technology ; Physical Sciences
类目[WOS]	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
研究领域[WOS]	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
关键词[WOS]	SUPPORT VECTOR MACHINE ; GENETIC ALGORITHM ; SLOPE STABILITY ; REGRESSION ; TUTORIAL ; NETWORKS ; FAILURE ; TIME
收录类别	SCI
原文出处	http://www.nat-hazards-earth-syst-sci.net/14/525/2014/nhess-14-525-2014.html
语种	英语
WOS记录号	WOS:000334093600003
公开日期	2014-12-02
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/7469]
专题	成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
作者单位	1.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
推荐引用方式 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.