Volume 7 Number 5 (May 2012)
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JCP 2012 Vol.7(5): 1073-1079 ISSN: 1796-203X
doi: 10.4304/jcp.7.5.1073-1079

RVM based on PSO for Groundwater Level Forecasting

Weiguo Zhao1, Yanfeng Gao1, Chunliu Li2
1Hebei University of Engineering, Handan 056038, China
2College of Urban Construction, Hebei Normal University of Science&Technology, Qinhuangdao 066004, China


Abstract—Relevance Vector Machine (RVM) is a novel kernel method based on Sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, the relevance vectors automatically determined. In this paper, a new RVM model optimized by Particle Swarm Optimization (PSO) is proposed, and it is applied to groundwater level forecasting. The simulation experiments demonstrate that the proposed method can reduce significantly both relative mean error and root mean squared error of predicted groundwater level. Moreover, the model achieved is much sparser than its counterpart, so the RVM based on PSO is applicable and performs well for groundwater data analysis.

Index Terms—Relevance Vector Machine, Particle Swarm Optimization, Support Vector Machine, groundwater level forecasting.

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Cite: Weiguo Zhao, Yanfeng Gao, Chunliu Li, "RVM based on PSO for Groundwater Level Forecasting," Journal of Computers vol. 7, no. 5, pp. 1073-1079, 2012.

General Information

ISSN: 1796-203X
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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