LS-SVM Prediction Model Based on Phase Space Reconstruction for Dam Deformation

He Zhi Liu; Song Lin Wang; Jing Yang Liu

doi:10.4028/www.scientific.net/AMR.663.55

Paper Titles

Vibration Table Test and Computer Simulation Analysis of Reinforced Concrete Eccentric Frame - Shear Wall Structures
p.31

Exploratory Study on the Development of Combination of Hotel and Apartment
p.37

Push-over Analysis on the Failure Mechanism of Reinforced Concrete Frame Structure
p.42

Research on the Shear Nails on the Arch Foot of an Oblique Cross Steel Box Arch Bridge
p.49

LS-SVM Prediction Model Based on Phase Space Reconstruction for Dam Deformation
p.55

The Effect of New Pouring Precooling Concrete on Lower Layer Concrete in Different Seasons
p.60

CTOD Fracture Toughness Test for Welded Joints of Steel
p.65

Study of the Concrete Block Temperature Intelligent Prediction Model during the Construction
p.68

The Seismic Analysis of Frame-Supported Shear Wall Structures with High Transfer Floor
p.72

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 663LS-SVM Prediction Model Based on Phase Space...

LS-SVM Prediction Model Based on Phase Space Reconstruction for Dam Deformation

Abstract:

This paper represents a Least-Square Support Vector Machine (LS-SVM) model based on the phase space reconstruction for forecasting nonlinear time series of dam deformation. Before training the LS-SVM, a time-delay reconstruction of phase space was made to view the dynamics of the monitoring data by using C-C method. And then the LS-SVM, which is a nonlinear, black-box regression method, was used for prediction. And the accuracy of this employed approach was examined by comparing it with multiple regression method. The experimental results indicate that the forecasting performance of the proposed method is significantly superior to that of the traditional multiple regression method.

You might also be interested in these eBooks

Health, Structure, Material and Environment

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 663)

Pages:

55-59

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.663.55

Citation:

Cite this paper

Online since:

February 2013

Authors:

He Zhi Liu, Song Lin Wang, Jing Yang Liu

Keywords:

Dam Deformation Prediction, Least Squares Support Vector Machines (Ls-SVM), Phase Space Reconstruction

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] C.S. GU, Z.R. WU: Safety Monitoring of Dams and Dam Foundations-Theories & Methods and Their Application (Hohai University Press, China 2006).

Google Scholar

[2] S.Y. Wang, G.H. Liu, W.G. Du, J.Y. Shi, F. Dong: Journal of Hydraulic Engineering, Vol. 7 (1999), pp.22-26.

Google Scholar

[3] S. H. Hwang, D.H. Ham, and J. H. Kim: KSCE Journal of Civil Engineering. Vol. 16 (2012), pp.887-882.

Google Scholar

[4] S.H. Wang, G.H. Liu, Z.R. Bao and W.G. Du: Hydropower Automation and Dam Monitoring, Vol. 27 (2003), pp.45-47.

Google Scholar

[5] D. S. K. Karunasinghe, S.Y. Liong: Journal of Hydrology, Vol. 323 (2006), pp.92-105.

Google Scholar

[6] F.W. Zhang Y.B. Li: Advances in FCCS, Vol. 2(2012), pp.541-545.

Google Scholar

[7] Y.S. Wang, W.H. Liu, L.B. Yang and Y. Sun: Journal of Naval Aeronautical and Astronautically University. Vol. 24 (2009), pp.283-288.

Google Scholar

[8] X.W. Wu, L.S. Suo and Z.J. Wang: Jouranl of System Simulation, Vol. 23 (2011), pp.2556-2559.

Google Scholar

[9] J.H. Lv, J.A. Lu and S.H. Chen: Chaotic Time Series Analysis and Its Application( Wuhan University Press, China 2002).

Google Scholar