Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31532
Drainage Prediction for Dam using Fuzzy Support Vector Regression

Authors: S. Wiriyarattanakun, A. Ruengsiriwatanakun, S. Noimanee


The drainage Estimating is an important factor in dam management. In this paper, we use fuzzy support vector regression (FSVR) to predict the drainage of the Sirikrit Dam at Uttaradit province, Thailand. The results show that the FSVR is a suitable method in drainage estimating.

Keywords: Drainage Estimation, Prediction.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1026


[1] B.P. Parida a,*, D.B. Moalafhi b, P.K. Kenabatho "Forecasting Runoff coefficients using ANN for water resources management: The case of Notwane catchment in Eastern Botswana " Physics and Chemistry of the Earth 31 , 2006, pp.928-934.
[2] T. gtokelj, R Golob "Application of neural networks for hydro power plant water inflow forecasting " 2000 IEEE. Neurel-2000, 5th Seminar on Neural Network Applications in Electrical Engineering.
[3] Y. B. Dibike and D. P. Solomatlne. River Flow Forecasting Using Artificial Neural Networks. Phys. Chem. Earth (B), Vol. 26, No. 1, 2001, pp. 1-7,
[4] Vapnik VN, GolowichSE, Smola AJ. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 1996, 9:281-7.
[5] S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: NNSP-97: Neural Networks for Signal Processing VII: Proceedings of the IEEE Signal Processing Society Workshop, Amelia Island, FL, USA ,1997, pp.511-520.
[6] Francis E.H. Tay , Lijuan Cao "Application of support vector machines in financialtime series forecasting" Omega 29 , 2001, pp. 309-317.
[7] Yongsheng Ding , Xinping Song , Yueming Zen "Forecasting financial condition of Chinese listed companies based on support vector machine " Expert Systems with Applications , 2007, pp23-32,.
[8] U. Thissen, R. van Brakel, A.P. de Weijer, W.J. Melssen, L.M.C. Buydens "Using support vector machines for time series prediction " Chemometrics and Intelligent Laboratory Systems 69, 2003, pp.35- 49.
[9] Lt. Udomsak Boonprasert R.N. "Development of the Ocean Model for Search and Rescue Using Support Vector Machine" master's thesis, Dept. Electrical Engineering,Univ. Chiang mai,2003
[10] Sivapragasam, C., Liong, S.-Y., Pasha, M.F.K., Rainfall andRunoff forecasting with SSA-SVM approach. Journal of Hydroinformatics 3(3), 2001, pp.141-152,.
[11] Bray, M., Han, D.,. Identification of support vector machines for Runoff modeling. Journal of Hydroinformatics 6 (4), 2004, pp.265-280.
[12] Sivapragasam, C., Liong, S.-Y., Identifying optima training data set - a new approach. In: Liong, S.Y.,Phoon, K.K., Babovic, V. (Eds.), Proceedings of the Sixth International Conference on Hydroinformatics, Singapore, 2004.
[13] V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, USA, 1998.
[14] B. Sch¨olkopf, A.J. Smola, Learning with Kernels, MIT Press, Cambridge,2002.
[15] N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.