Drainage Prediction for Dam using Fuzzy Support Vector Regression

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.




References:
[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.