Email this article RVM based on PSO for Groundwater Level Forecasting
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.
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References
[1].P. D. Sreekanth1, N. Geethanjali, P. D. Sreedevi3, Shakeel Ahmed, N. Ravi Kumar and P. D. Kamala Jayanthi, “Forecasting groundwater level using artificial neural networks,” CURRENT SCIENCE, 2009,96(7): 933-939.
[2]. Abedalrazq Khalil,Mohammad N. Almasri,Mac McKee, and Jagath J, Kaluarachchi1, “Applicability of statistical learning algorithms in groundwater quality modeling,” Water Resurces Research, 2005.
[3]. Noslen Hernández, Isneri Talavera, Angel Dago, Rolando J. Biscay, Marcia M. Castro Ferreira, and Diana Porro, “Relevance vector machines for multivariate calibration purposes,” Chemometrics, 2008(22):386-694.
[4]. Abedalrazq F Khalil,Mac McKee,Mariush Kemblowski,Tirusew Asefa, and Luis Bastidas, “Multiobjective analysis of chaotic dynamic systems with spame learning machines,” Advances in Water Resources, 2006(29):72-88.
[5]. Tipping ME, “Sparse Bayesian learning and the relevance vector machine,” Journal of machine learning research, 2001, 1(3):211-244.
[6]. M. E. Tipping and A. C. Faul, “Fast marginal likelihood maximisation for sparse Bayesian models,” Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6, 2003.
[7]. Kennedy, J. and Eberhart, R.C, “Particle swarm optimization,” Proc. IEEE Int'l. Conf. on Neural Networks, IV, 1995, 1942-1948.
[8]. Li Bingyu, Xiao Yunshi, and Wang Lei, “A hybrid Particle swarm optimization algorithm for solving complex functions with high dimension,” Informalion and Coarrol” 2004, (33): 27-30.
[9]. Amany El-Zonkoly, “PARTICLE SWARM OPTIMIZATION FOR SOLVING THE PROBLEM OF TRANSMISSION SYSTEMS AND GENERATION EXPANSION,” Nansoura Engineering 2005, 30(1):15-20.
[10]. Radoslav Goldman, et al, “Candidate Markers for the Detection of Hepato Cellular Carcinoma in Low-mofraction of Serum,” Carcinogenesis, 2007, 28(10):2149-2153.
http://dx.doi.org/10.1093/carcin/bgm177
PMid:17724376 PMCid:2204039
[11]. Wensheng Wang and Jing Ding, “Wavelet Network Model and Its Application to the Prediction of Hydrology,” Nature and Science, 2003, 1(1): 67-71.
[12]. Qing G, Ding J, and Liu G, “Self-adapted BP algorithm and its application to flood forecast of river,” Advance in Water Science, 2002, 13(1):37-41.
[13]. WANG Hongrui, YE Letian, and LIU Changming, et al, “Problems in wavelet analysis of hydrologic series and some suggestions on improvement,” Progress In Natural Science, 2007, 17(1): 80-86, 2007
[14]. Hai Shen, Xiaoming Zhou, Xiaoming Zhou, and Xiaoming Zhou, “Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization,” Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, 2009, 497-504
[15]. C.-H. Wu, Jan-Ming Ho, and D. T. Lee, “Travel-time prediction with support vector regression,” IEEE Trans. Intelligent Transportation Systems, 2004, 5(4):276-281.
http://dx.doi.org/10.1109/TITS.2004.837813
[16]. CAO Cheng-tao, and XU Jian-min, “SVM based on PSO and its application in traffic flow predication,” Computer Engineering and Applications, 2007, 432(15): 12-14.
[17]. Shivam, Tripathi, and Rao S, “On selection of kernel parameters in relevance vector machines for hydrologic applications,” Stochastic Environmental Research and Risk Assessment, 2007, 21(6):747-764.
http://dx.doi.org/10.1007/s00477-006-0087-9
[18]. Kropotov Dmitry, and Ptashko Nikita, “On Kernel Selection in Relevance Vector Machines Using Stability Principle,” Proceedings of the 18th International Conference on Pattern Recognition, 2007 (4): 233-236.
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