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

Content Provider	Springer Nature Link
Author	Cai, Zhenglong Xu, Weiya Meng, Yongdong Shi, Chong Wang, Rubin
Copyright Year	2015
Abstract	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.
Starting Page	637
Ending Page	646
Page Count	10
File Format	PDF
ISSN	14359529
Journal	Bulletin of the International Association of Engineering Geology - Bulletin de l'Association Internationale de Géologie de l'Ingénieur
Volume Number	75
Issue Number	2
e-ISSN	14359537
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-10-05
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Landslide displacement prediction Multiple factors Wavelet decomposition Least-squares support vector machine Geotechnical Engineering & Applied Earth Sciences Geoengineering, Foundations, Hydraulics Geoecology/Natural Processes Nature Conservation
Content Type	Text
Resource Type	Article
Subject	Geology Geotechnical Engineering and Engineering Geology

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