Application of Support Vector Machine in Lake Water Level Prediction
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 3
Abstract
This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to ahead. The results are compared with a widely used neural network model called a multilayer perceptron (MLP) and with a conventional multiplicative seasonal autoregressive model (SAR). Overall, the SVM showed good performance and is proved to be competitive with the MLP and SAR models. For a 3- to 12-month-ahead prediction, the SVM model outperforms the two other models based on root-mean square error and correlation coefficient performance criteria. Furthermore, the SVM exhibits inherent advantages due to its use of the structural risk minimization principle in formulating cost functions and of quadratic programming during model optimization. These advantages lead to a unique optimal and global solution compared to conventional neural network models.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers gratefully acknowledge the financial support given by the Natural Sciences and Engineering Research Council of Canada to the second writer for this study. The writers also gratefully acknowledge the helpful comments of Dr. Yonas B. Dibike.
References
Belousov, A. I., Verzakov, S. A., and von Frese, J. (2002). “Application aspects of support vector machines.” J. Chemom., 16, 482–489.
Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis: Forecasting and control, Holden-Day, Oakland, Calif.
Cao, L. (2003). “Support vector machines experts for time series forecasting.” Neurocomputing, 51, 321–339.
Chatfield, C. (1980). The analysis of time series: An introduction, 2nd Ed., Chapman & Hall, London.
Cherkassky, V., and Ma, Y. (2004). “Practical selection of SVM parameters and noise estimation for SVM regression.” Neural Networks, 17, 113–126.
Coulibaly, P., Anctil, F., and Bobée, B. (2001). “Multivariate reservoir inflow forecasting using temporal neural networks.” J. Hydrologic Eng., 6(5), 367–376.
Dibike, Y. B., Velickov, S., Solomatine, D., and Abbott, M. B. (2001). “Model induction with support vector machines: Introduction and applications.” J. Comput. Civ. Eng., 15(3), 208–216.
Haykin, S. (1999). Neural networks, 2nd Ed., Prentice-Hall, Upper Saddle River, N.J.
Kim, K. (2003). “Financial time series forecasting using support vector machines.” Neurocomputing, 55, 307–319.
Mukherjee, S., Osuna, E., and Girosi, F. (1997). “Nonlinear prediction of chaotic time series using support vector machine.” Proc., 7th IEEE Workshop on Neural Networks for Signal Processing, Institute of Electrical and Electronics Engineers, New York, 511–519.
Müller, K. R., Smola, A., Ratsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V. (1997). “Predicting time series with support vector machines.” Proc., Int. Conf. on Artificial Neural Networks, Springer, Berlin.
NeuroSolutions user’s manual; version 4.21. (2002). NeuroDimension, Inc., Gainesville, Fla.
Osuna, E., Freund, R., and Girosi, F. (1997). “An improved training algorithm for support vector machines.” Proc., 7th IEEE Workshop on Neural Networks for Signal Processing, Institute of Electrical and Electronics Engineers, New York, 276–285.
Privalsky, V. (1992). “Lake Erie water level variations.” J. Great Lakes Res., 18(1), 236–243.
Sánchez A. V. D., (2003). “Advanced support vector machines and kernel methods.” Neurocomputing, 55, 5–20.
Saunders, C., Stitson, M. O., Weston, J., Bottou, L., Schölkopf, B., and Smola, A. (1998). “Support vector machine: Reference manual.” Royal Holloway Tech. Rep. CSD-TR-98-03, Royal Holloway College, Univ. of London, Egham, Surrey, U.K.
Smola, A. J., and Schölkopf, B. (1998). “A tutorial on support vector regression.” NeuroCOLT Technical Rep. NC-TR-98-030, Royal Holloway College, Univ. of London, Egham, Surrey, U.K. ⟨http://www.kernel-machines.org/⟩.
Tay, F. E. H., and Cao, L. (2001). “Application of support vector machines in financial time series forecasting.” Omega, 29, 309–317.
Thissen, U., van Brakel, R., de Weijer, A. P., Melssen, W. J., and Buydens, L. M. C. (2003). “Using support vector machines for time series prediction.” Chemom. Intell. Lab. Syst., 69, 35–49.
Vapnik, V. (1995). The nature of statistical learning theory, Springer, New York.
Vapnik, V. (1998). Statistical learning theory, Wiley, New York.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 3 • May 2006
Pages: 199 - 205
Copyright
© 2006 ASCE.
History
Received: Mar 9, 2004
Accepted: May 16, 2005
Published online: May 1, 2006
Published in print: May 2006
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.