Support vector regression for real-time flood stage forecasting

Journal of Hydrology

Volumn 328, Issue 3-4, 2006, Pages 704-716

  Yu, Pao Shan ^a   Chen, Shien Tsung ^a   Chang, I Fan ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Flood forecasting; Parameter optimization; Support vector regression; Water stage

Indexed keywords

ARTIFICIAL INTELLIGENCE; HYDROLOGY; MATHEMATICAL MODELS; OPTIMIZATION; REAL TIME SYSTEMS; SENSITIVITY ANALYSIS; STATISTICAL METHODS; WEATHER FORECASTING;

FLOOD FORECASTING; PARAMETER OPTIMIZATION; SUPPORT VECTOR REGRESSION; WATER STAGE;

FLOODS;

ARTIFICIAL INTELLIGENCE; FLOODS; HYDROLOGY; MATHEMATICAL MODELS; OPTIMIZATION; REAL TIME SYSTEMS; SENSITIVITY ANALYSIS; STATISTICAL METHODS; WEATHER FORECASTING;

FLOOD FORECASTING; FORECASTING METHOD; HYDROLOGICAL MODELING; REGRESSION ANALYSIS;

ASIA; EURASIA; FAR EAST; TAIWAN;

EID: 33746916489     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2006.01.021     Document Type: Article

References (30)

1. Bazartseren B., Hildebrandt G., and Holz K.-P. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55 (2003) 439-450
2. Bray M., and Han D. Identification of support vector machines for runoff modeling. Journal of Hydroinformatics 6 4 (2004) 265-280
3. Chang F.-J., and Chen Y.-C. Estuary water-stage forecasting by using radial basis function neural network. Journal of Hydrology 270 (2003) 158-166
4. Chang, C.-C., Lin, C.-J., 2001. LIBSVM: A Library for Support Vector Machines (Version 2.71, November 2004). Software available at: .
5. Chau K. River stage forecasting with particle swarm optimization. Lecture Notes in Computer Science 3029 (2004) 1166-1173
6. Cherkassky V., and Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (2004) 113-126
7. Choy K.Y., and Chan C.W. Modelling of river discharges and rainfall using radial basis function networks based on support vector regression. International Journal of Systems Science 34 14-15 (2003) 763-773
8. Dibike Y.B., Velickov S., Solomatine D., and Abbott M.B. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering 15 3 (2001) 208-216
9. Fletcher R. Practical Methods of Optimization. second ed. (1987), Wiley, New York
10. Franchini M., and Lamberti P. A flood routing Muskingum type simulation and forecasting model based on level data alone. Water Resources Research 30 7 (1994) 2183-2196
11. Han D., and Cluckie I. Support vector machines identification for runoff modeling. In: Liong S.Y., Phoon K.K., and Babovic V. (Eds). Proceedings of the Sixth International Conference on Hydroinformatics. Singapore, 21-24 June 2004 (2004), World Scientific Publishing Co., Singapore
12. Hsu, C.-W., Chang, C.-C., Lin, C.-J., 2003. A Practical Guide to Support Vector Classification. Available at: .
13. Kelly K.S., and Krzysztofowicz R. Precipitation uncertainty processor for probabilistic river stage forecasting. Water Resources Research 36 9 (2000) 2643-2653
14. Krzysztofowicz R. Integrator of uncertainties for probabilistic river stage forecasting: precipitation-dependent model. Journal of Hydrology 249 (2001) 69-85
15. Krzysztofowicz R. Bayesian system for probabilistic river stage forecasting. Journal of Hydrology 268 (2002) 16-40
16. Krzysztofowicz R., and Herr H.D. Hydrologic uncertainty processor for probabilistic river stage forecasting: Precipitation-dependent model. Journal of Hydrology 249 (2001) 46-68
17. Krzysztofowicz R., and Kelly K.S. Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resources Research 36 11 (2000) 3265-3277
18. Liong S.-Y., Lim W.-H., and Paudyal G.N. River stage forecasting in Bangladesh: neural network approach. Journal of Computing in Civil Engineering 14 1 (2000) 1-8
19. Liong S.-Y., and Sivapragasam C. Flood stage forecasting with support vector machines. Journal of the American Water Resources Association 38 1 (2002) 173-196
20. Mattera D., and Haykin S. Support vector machines for dynamic reconstruction of a chaotic system. In: Schölkopf B., Burges J., and Smola A. (Eds). Advances in Kernel Methods Support Vector Machine (1999), MIT Press, Cambridge, MA
21. See L., and Openshaw S. Applying soft computing approaches to river level forecasting. Hydrological Sciences Journal 44 5 (1999) 763-778
22. See L., and Openshaw S. A hybrid multi-model approach to river level forecasting. Hydrological Sciences Journal 45 4 (2000) 523-536
23. Seibert J. On the need for benchmarks in hydrological modelling. Hydrological Processes 15 (2001) 1063-1064
24. Sivapragasam C., and Liong S.-Y. Identifying optimal training data set - a new approach. In: Liong S.Y., Phoon K.K., and Babovic V. (Eds). Proceedings of the Sixth International Conference on Hydroinformatics. Singapore, 21-24 June 2004 (2004), World Scientific Publishing Co., Singapore
25. Sivapragasam C., and Liong S.-Y. Flow categorization model for improving forecasting. Nordic Hydrology 36 1 (2005) 37-48
26. Sivapragasam C., Liong S.-Y., and Pasha M.F.K. Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics 3 3 (2001) 141-152
27. Thirumalaiah K., and Deo M.C. River stage forecasting using artificial neural networks. Journal of Hydrologic Engineering 3 1 (1998) 26-32
28. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
29. Vapnik V.N. An overview of statistical learning theory. IEEE Transactions on Neural Networks 10 5 (1999) 988-999
30. Yu X., Liong S.-Y., and Babovic V. EC-SVM approach for real-time hydrologic forecasting. Journal of Hydroinformatics 6 3 (2004) 209-223