Prediction of landslide displacement based on GA-LSSVM with multiple factors

被引：105

作者：

Cai, Zhenglong ^{[1
,2
]}

Xu, Weiya ^{[2
]}

Meng, Yongdong ^{[1
,3
]}

Shi, Chong ^{[1
,2
]}

Wang, Rubin ^{[1
,2
]}

机构：

[1] Hohai Univ, Res Inst Geotech Engn, Nanjing 210098, Jiangsu, Peoples R China

[2] Hohai Univ, Key Lab, Minist Educ Geomech & Embankment Engn, Nanjing 210098, Jiangsu, Peoples R China

[3] Three Gorges Univ, Hubei Key Lab Construct & Management Hydropower E, Yichang 443002, Peoples R China

来源：

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT | 2016年 / 75卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Landslide displacement prediction; Multiple factors; Wavelet decomposition; Least-squares support vector machine; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL-NETWORKS; DECOMPOSITION; MODEL; SVM; OPTIMIZATION;

D O I：

10.1007/s10064-015-0804-z

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper presents a new model for predicting the displacement of a landslide based on the least-squares support vector machine (LSSVM) with multiple factors and a genetic algorithm (GA) is used to optimize the parameters of the LSSVM model. First, based on original monitoring displacement data, single factor GA-LSSVM models are established with and without wavelet decomposition. Second, from the analysis of the basic characteristics of a landslide, the main influencing factors of landslide displacement are identified according to their correlation coefficients. A multifactor GA-LSSVM model is then established for the prediction of landslide displacement. A case study of a landslide reveals that wavelet decomposition can efficiently improve the prediction accuracy of the GA-LSSVM model. In addition, the multifactor GA-LSSVM model performs consistently better than the single factor models for the same measurements.

引用

页码：637 / 646

页数：10

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