Forecasting performance of LS-SVM for nonlinear hydrological time series

KSCE Journal of Civil Engineering

Volumn 16, Issue 5, 2012, Pages 870-882

  Hwang, Seok Hwan ^a   Ham, Dae Heon ^a   Kim, Joong Hoon ^b  

^a Korea Institute of Civil Engineering and Building Technology   (South Korea)

^b Korea University   (South Korea)

Author keywords

forecasting; forecasting performance; support vector machine

Indexed keywords

CORRELATION COEFFICIENT; DAILY WATER DEMAND; FORECASTING ACCURACY; FORECASTING PERFORMANCE; HYDROLOGICAL TIME-SERIES; LEAST-SQUARE SUPPORT VECTOR MACHINES; MULTIPLE LINEAR REGRESSIONS; PREDICTION MODEL; PREDICTION PERFORMANCE; STATISTICAL LEARNING THEORY; THREE MODELS;

FORECASTING; HEURISTIC METHODS; LEARNING ALGORITHMS; LINEAR REGRESSION; MATHEMATICAL MODELS; TIME SERIES;

SUPPORT VECTOR MACHINES;

EID: 84863306895     PISSN: 12267988     EISSN: 19763808     Source Type: Journal    
DOI: 10.1007/s12205-012-1519-3     Document Type: Article

References (42)

1. Anctil, F., Michel, C., Perrin, C., and Vazken Andreassian, V. (2004). "A soil moisture index as an auxiliary ANN input for stream flow forecasting." Journal of Hydrology, Vol. 286, Nos. 1-4, pp. 155-167.
2. Anderson-Sprecher, R. (1994). "Model comparison and R2." The American Statistician, Vol. 48, No. 2, pp. 113-117.
3. Armstrong, J. S. and Collopy, F. (1992). "Error measures for generalizing about forecasting methods: Empirical comparisons." International Journal of Forecasting, Vol. 8, Issue 1, pp. 69-80.
4. ASCE (1993). "Criteria for evaluation of watershed models." Journal of Irrigation and Drainage Engineering, Vol. 119, No. 3, pp. 429-442.
5. ASCE (ASCE Task Committee on the Application of Artificial Neural Networks in Hydrology) (2000a). "Artificial neural networks in hydrology. I: Preliminary concepts." Journal of Hydrologic Engineering, Vol. 5, Issue 2, pp. 115-123.
6. ASCE (ASCE Task Committee on the Application of Artificial Neural Networks in Hydrology) (2000b). "Artificial neural networks in hydrology. II: Hydrologic applications." Journal of Hydrologic Engineering, Vol. 5, Issue 2, pp. 124-137.
7. Asefa, T. and Kemblowski, M. W. (2002). "Support vector machines approximation of flow and transport models in initial groundwater contamination network design." Eos. Trans. AGU, Vol. 83, No. 47, Fall Meet. Suppl., Abstract H72D-0882.
8. Bracmort, K. S., Arabi, M., Frankenberger, J. R., Engel, B. A., and Arnold, J. G. (2006). "Modeling long-term water quality impact of structural BMPS." Trans. ASAE, Vol. 49, No. 2, pp. 367-384.
9. Chau, K. W., Wu, C. L., and Li, Y. S. (2005). "Comparison of several flood forecasting models in Yangtze River." Journal of Hydrologic Engineering, ASCE, Vol. 10, No. 6, pp. 485-491.
10. Cheng, C. T., Ou, C. P., and Chau, K. W. (2002). "Combining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration." Journal of Hydrology, Vol. 268, Nos. 1-4, pp. 72-86.
11. Cheng, C. T., Wang, W. C., Xu, D. M., and Chau, K. W. (2008). "Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos." Water Resources Management, Vol. 22, No. 7, pp. 895-909.
12. Dawson, C. W., Abrahart, R. J., and See, L. M. (2007). "HydroTest: A web-based toolbox of evaluation metrics for the standardized assessment of hydrological forecasts." Environmental Modelling & Software, Vol. 22, No. 7, pp. 1034-1052.
13. Dibike, Y. B., Velickov, S., Solomatine, D. P., and Abbott, M. B. (2001). "Model induction with support vector machines: Introduction and applications." Journal of Computing in Civil Engineering, ASCE, Vol. 15, No. 3, pp. 208-216.
14. Donigian, A. S., Imhoff, J. C., and Bicknel, B. R. (1983). Predicting water quality resulting from agricultural nonpoint-source pollution via simulation HSPF, In Agricultural Management and Water Quality 200-249, Ames, Iowa: Iowa State University Press.
15. Golub, G. H. and van Loan, C. F. (1989). Matrix computations, Johns Hopkins University Press, Baltimore, MD.
16. Gunn, S. (1998). Support vector machines for classification and regression, ISIS Technical Report, University of Southampton.
17. Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1999). "Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration." Journal of Hydrologic Engineering, Vol. 4, No. 2, pp. 135-143.
18. Hyndman, R. J. and Koehler, A. B. (2006). "Another look at measures of forecast accuracy." International Journal of Forecasting, Vol. 22, No. 4, pp. 679-688.
19. Kim, Y. O. (1994). Korea's climate and culture, Ewha Womans University Press, Seoul, 104.
20. Klemes V. (1986). "Operational testing of hydrological simulation models." Hydrological Sciences Journal, Vol. 31, No. 3, pp. 13-24.
21. Legates, D. R. and McCabe, G. J. (1999). "Evaluating the use of 'goodness-of fit' measures in hydrologic and hydroclimatic model validation." Water Resources Research, Vol. 35, No. 1, pp. 233-241.
22. Lin, J. Y., Cheng, C. T., and Chau, K. W. (2006). "Using support vector machines for long-term discharge prediction." Hydrological Sciences Journal, Vol. 51, No. 4, pp. 599-612.
23. Liong, S. Y. and Sivapragasm, C. (2002). "Flood stage forecasting with SVM." Journal of American Water Resources Association, Vol. 38, No. 1, pp. 173-186.
24. Motovilov, Y. G., Gottschalk, L., England, K., and Rodhe, A. (1999). "Validation of distributed hydrological model against spatial observations." Agric. Forest Meteorology, Vol. 98-99, pp. 257-277.
25. Nash J. E. and Sutcliffe J. V. (1970). "River flow forecasting through conceptual models, Part I - A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.
26. Osuna, E., Freund, R., and Girosi, F. (1997). "An improved training algorithm for support vector machines." Proc. of the IEEE Workshop on Neural Networks for Signal Processing VII, New York, pp. 276-285.
27. Pearson, K. (1896). "Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia." Philosophical Transactions of the Royal Society of London Series A, Vol. 187, pp. 253-318.
28. Pebesma, E. J., Switzer, P., and Loague, K. (2005). "Error analysis for the evaluation of model performance: rainfall-runoff event times series data." Hydrological Processes, Vol. 19, No. 8, pp. 1529-1548.
29. Pelckmans, K., Suykens, J. A. K., van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., and Vandewalle, J. (2003). SVMlab toolbox user's guide version 1. 5, K. U. Leuven.
30. Ramanarayanan, T. S., Williams, J. R., Dugas, W. A., Hauck, L. M., and McFarland, A. M. S. (1997). Using APEX to identify alternative practices for animal waste management, ASAE Paper No. 972209, St. Joseph: Michigan.
31. Saleh, A., Arnold, J. G., Gassman, P. W., Hauk, L. M., Rosenthal, W. D., Williams, J. R., and MacFarlan, A. M. S. (2000). "Application of SWAT for the upper North Bosque River watershed." Transaction of the ASAE, Vol. 43, No. 5, pp. 1077-1087.
32. Santhi, C., Arnold, J. G., Williams, J. R., Dugas, W. A., Srinivasan, R., and Hauck, L. M. (2001). "Validation of the SWAT model on a large river basin with point and nonpoint sources." Journal of American Water Resources Association, Vol. 37, No. 5, pp. 1169-1188.
33. Scholkopf, B. and Smola A. (2002). Learning with kernels, MIT Press, Cambridge MA.
34. Seibert, J. (2001). "On the need for benchmarks in hydrological modeling." Hydrological Processes, Vol. 15, No. 6, pp. 1063-1064.
35. Singh, J., Knapp, H. V., and Demissie, M. (2004). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT, ISWS CR2004-08.
36. Suykens, J. A. K. and Vandewalle, J. (1999). "Least squares support vector machine classifiers." Neural Processing Letters, Vol. 9, No. 3, pp. 293-300.
37. Suykens, J. A. K., van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J. (2002). Least squares support vector machines, WorldScientic, Singapore.
38. van Liew, M. W., Veith, T. L., Bosch, D. D., and Arnold, J. G. (2007). "Suitability of SWAT for the conservation effects assessment project: A comparison on USDA-ARS experimental watersheds." Journal of Hydrologic Engineering, Vol. 12, No. 2, pp. 173-189.
39. Vapnik, V. (1995). The nature of statistical learning theory, Springer, New York.
40. Vapnik, V. (1998). Statistical learning theory, Wiley, New York.
41. Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L. (2009). "A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series." Journal of Hydrology, Vol. 374, Nos. 3-4, pp. 294-306.
42. Wu, C. L., Chau, K. W., and Li, Y. S. (2009). "Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques." Water Resources Research, Vol. 45, No. W08432, doi: 10. 1029/2007WR 006737.