Forecasting of hydrologic time series with ridge regression in feature space

Journal of Hydrology

Volumn 332, Issue 3-4, 2007, Pages 290-302

  Yu, Xinying ^a   Liong, Shie Yui ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Chaotic technique; Evolutionary algorithm; Features approximation; Gaussian kernel; Support vector machine; Time series analysis

Indexed keywords

CHAOS THEORY; DATA MINING; EVOLUTIONARY ALGORITHMS; REGRESSION ANALYSIS; SEARCH ENGINES; TIME SERIES ANALYSIS; WEATHER FORECASTING;

CHAOTIC TECHNIQUE; FEATURES APPROXIMATION; GAUSSIAN KERNEL; SUPPORT VECTOR MACHINE;

HYDROLOGY;

CHAOS THEORY; DATA MINING; EVOLUTIONARY ALGORITHMS; HYDROLOGY; REGRESSION ANALYSIS; SEARCH ENGINES; TIME SERIES ANALYSIS; WEATHER FORECASTING;

CATCHMENT; DATA MINING; REGRESSION ANALYSIS; RIVER FLOW; RUNOFF; TIME SERIES ANALYSIS;

DENMARK; EURASIA; EUROPE; FYN; MISSISSIPPI RIVER; NORTH AMERICA; NORTHERN EUROPE; SCANDINAVIA; TRYGGEVAELDE RIVER; UNITED STATES;

EID: 33845702662     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2006.07.003     Document Type: Article

References (27)

1. Anctil F., Michel C., Perrin C., and Andréassian V. A soil moisture index as an auxiliary ANN input for stream flow forecasting. Journal of Hydrology 286 (2004) 155-167
2. Babovic, V., Keijzer, M., & Bundzel, M. (2000). From global to local modelling: A case study in error correction of deterministic models. In: Proceedings of fourth international conference on hydroinformatics, Iowa City, USA: CD-ROM, IAHR.
3. Casdagli M. Nonlinear prediction of chaotic time series. Physica D 35 (1989) 335-356
4. Collobert R., and Bengio S. SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research 1 (2001) 143-160
5. Cover T., and Hart P. Nearest-neighbour pattern classification. IEEE Transactions on Information Theory 13 (1967) 21-27
6. Dibike Y.B., Velickov S., Solomatine D.P., and Abbott M.B. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering-ASCE 15 3 (2001) 208-216
7. Doan C.D., Liong S.Y., and Karunasingha D.S.K. Deriving effective and efficient data set with subtractive clustering method and genetic algorithm. Journal of Hydroinformatics 7 4 (2005) 219-233
8. Duan Q., Sorooshian S., and Gupta V.K. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resource Research 28 4 (1992) 1015-1031
9. Gibson J.F., Farmer J.D., Casdagli M., and Eubank S. An analytical approach to practical state space reconstruction. Physica D 57 (1992) 1-30
10. Girolami M. Orthogonal series density estimation and the kernel eigenvalue problem. Neural Computation 14 (2002) 669-688
11. Joachims T. Making large-scale SVM learning practical. In: Schölkopf B., Burges C., and Smola A. (Eds). Advances in kernel methods - Support vector learning (1999), MIT Press 169-183
12. Karunanithi N., Grenney W.J., Whitley D., and Bovee K. Neural networks for river flow prediction. Journal of Computing in Civil Engineering 8 22 (1994) 201-220
13. Liong S.Y., and Sivapragasam C. Flood stage forecasting with SVM. Journal of the American Water Resources Association 38 1 (2002) 173-186
14. Liong, S. Y., Phoon, K. K., Pasha, M. F. K, & Doan, C. D. (2002). A robust and efficient scheme in search for optimal prediction parameters set in chaotic time series. First Asia Pacific DHI Software Conference, Bangkok, (http://www.mike11.com/bangkok2002/proceedings/papers%20bangkok/ba%20059/059.htm).
15. Mitchell T. Machine learning (1997), McGraw Hill
16. Phoon K.K., Islam M.N., Liaw C.Y., and Liong S.Y. Practical inverse approach for forecasting nonlinear hydrological time series. Journal of Hydraulic Engineering-ASCE 7 2 (2002) 116-128
17. Sivakumar B., Jayawardena A.W., and Fernando T.M.K.G. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology 265 (2002) 225-245
18. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., and Vandewalle J. Least squares support vector machines (2002), World Scientific Pub. Co., Singapore
19. Takens F. Dynamical systems and turbulence. Lecture notes in mathematics (Warwick) vol. 898 (1981), Springer, Berlin 366
20. Toth E., Brath A., and Montanari A. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 239 (2000) 132-147
21. Vapnik V.N. Principle of risk minimization for learning theory. Advances in neural information processing system vol. 4 (1992), Morgan Kaufman, San Meteo, CA pp. 831-838
22. Vapnik V., Golowich S., and Smola A. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M., Jordan M., and Petsche T. (Eds). Advances in neural information processing systems vol. 9 (1997), MIT Press, Cambridge, MA 281-287
23. Williams C.K.I., and Seeger M. The effect of the input density distribution on kernel-based classifiers. Proceedings of the seventeenth international conference on machine learning (2000), Morgan Kaufmann
24. Williams C.K.I., and Seeger M. Using the Nystrom methods to speed up kernel machines. Advances in neural information processing systems 13 (2001), MIT Press 682-688
25. Yu X.Y., Liong S.Y., and Babovic V. EC-SVM approach for real time hydrologic forecasting. Journal of Hydroinformatics 6 3 (2004) 209-223
26. Zaldívar J.M., Gutiérrez E., Galván I.M., Strozzi F., and Tomasin A. Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks. Journal of Hydroinformatics 2 (2000) 61-84
27. Zealand C.M., Burn D.H., and Simonovic S.P. Short term streamflow forecasting using artificial neural networks. Journal of Hydrology 214 (1999) 32-48