Application Study of Least Squares Support Vector Machines in Streamflow Forecast

Yan Zhao; Zeng Chuan Dong; Qing Hang Li

doi:10.4028/www.scientific.net/AMM.212-213.436

Paper Titles

Theoretical Investigation into Plunging Point of Density Current in Reservoir
p.413

Study on Water Level Prediction in the Ganjiang Valley
p.417

Analysis of Water Resources Carrying Capacity in Liao River Basin
p.423

Simulation and Analysis of Ocean Wave in the Northwest Pacific Ocean
p.430

Application Study of Least Squares Support Vector Machines in Streamflow Forecast
p.436

Analysis for Sediment Deposition and Erosion in Key Sections of River Reach in Chongqing City during the Natural Evolution
p.441

A Simple and Effective Prediction Model of Saline Intrusion in the Modaomen Estuary, the Pearl River Delta, China
p.446

Geotextile Recharge Well with Filter Layer
p.453

The Correlation of Dynamic Cone Penetration Blow Count of Wind Carried Sand vs. Surface Wave Velocity in West Bank of Qinghai Lake
p.457

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 212-213Application Study of Least Squares Support Vector...

Application Study of Least Squares Support Vector Machines in Streamflow Forecast

Abstract:

In this article, the Least Square Support Vector Machine(LS-SVM) regression analysis and prediction methods were briefly introduced. Radial basis kernel function was chosen and a streamflow forecast model based on the toolbox of Matlab was constructed. Then the model was validated with a case study. After the model validation with a case study, it could be seen that the prediction model shows high accuracy and convergence speed. According to the analysis of the results, it is feasible for LS-SVM utilization in streamflow forecast.

You might also be interested in these eBooks

Advances in Hydrology and Hydraulic Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 212-213)

Pages:

436-440

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.212-213.436

Citation:

Cite this paper

Online since:

October 2012

Authors:

Yan Zhao, Zeng Chuan Dong, Qing Hang Li

Keywords:

Kernel Function, Least Squares Support Vector Machines (Ls-SVM), Regression Analysis, Streamflow Prediction

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Feng-Ling T, Sheng W, Sheng-Cai Y, et al. Longyangxia Runoff Forecasting Based on LS-SVM[J]. JOURNAL OF CHINA HYDROLOGY. 2008, 28(4): 29-30.

Google Scholar

[2] Y L S, C S. Flood Stage Forecasting with SVM[J]. Journal of the American Water Resources Association. 2002, 38(1): 173-186.

Google Scholar

[3] J S A, B S. A Tutorial on Support Vector Regression [J]. Statistics and Computing. 2004, 14: 199-222.

Google Scholar

[4] V V. Statistical Learning Theory[M]. New York: Springer, 1998.

Google Scholar

[5] Jia L, Li W, Guang-Wen M, et al. Application of Least Squares Support Vector Machines in Runoff Forecast[J]. China Rural Water and Hydropower. 2008, 3(5): 8-10.

Google Scholar

[6] Xue-Jun F. Research and Application of LS-SVM[J]. Journal of Anqing Teachers College(Natural Science Edition). 2009, 15(1): 112-114.

Google Scholar

[7] Fang-Qiong L, Jian-Sheng W, Long J. Rainfall Forecasting Model Based on Least Square Support Vector Machine Regression Ensemble[J]. Journal of Tropical Meteorology. 2011, 27(4): 577-584.

Google Scholar

[8] Ping F, Rui-Hua S, Quan-Yin S, et al. The Application of Least Squares Support Vector Machine Regression in Water Quality Forecast of Xi'an Ba River[J]. Systems Engineering. 2011, 29(6): 113-116.

Google Scholar

[9] Zhongxiu Fan. Long-term Hydrological Forecast[M]. Nan Jing: Hohai University Press, 1999.

Google Scholar