Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods

Journal of Hydrology

Volumn 372, Issue 1-4, 2009, Pages 17-29

  Lin, Gwo Fong ^a   Chen, Guo Rong ^a   Huang, Pei Yu ^a   Chou, Yang Ching ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Artificial neural networks; Reservoir inflow forecasting; Reservoir operation system; Support vector machines; Typhoon characteristics

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; BACKPROPAGATION NETWORK; EFFECTIVE RESERVOIR; FORECASTING MODELS; FORECASTING PERFORMANCE; GENERALIZATION ABILITY; KEY INPUT; LONG LEADS; MODELING TECHNIQUE; RESERVOIR INFLOW; RESERVOIR INFLOW FORECASTING; RESERVOIR OPERATION SYSTEM; STATISTICAL LEARNING THEORY; TYPHOON CHARACTERISTICS;

BACKPROPAGATION; EDUCATION; FORECASTING; GEARS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CLIMATE MODELING; COMPARATIVE STUDY; FORECASTING METHOD; INFLOW; PERFORMANCE ASSESSMENT; RESERVOIR; TYPHOON; WARNING SYSTEM; WEATHER FORECASTING;

EID: 65649123113     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2009.03.032     Document Type: Article

References (21)

1. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering 5 2 (2000) 115-123
2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering 5 2 (2000) 115-123
3. Bae D.H., Jeong D.M., and Kim G. Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrological Sciences Journal 52 1 (2007) 99-113
4. Chang Y.T., Chang L.C., and Chang F.J. Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves. Hydrological Processes 19 7 (2005) 1431-1444
5. Chaves P., and Kojiri T. Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks. Advances in Water Resources 30 5 (2007) 1329-1341
6. Chaves P., and Kojiri T. Stochastic fuzzy neural network: case study of optimal reservoir operation. Journal of Water Resources Planning and Management - ASCE 133 (2007) 509-518
7. Coulibaly P., Hache M., Fortin V., and Bobee B. Improving daily reservoir inflow forecasts with model combination. Journal of Hydrologic Engineering 10 2 (2005) 91-99
8. Cristianini N., and Shaw-Taylor J. An Introduction to Support Vector Machines and other Kernel-based Learning Methods (2000), Cambridge University Press, New York
9. Lin G.F., and Chen L.H. A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology 289 1-4 (2004) 1-8
10. Lin G.F., and Chen L.H. Application of artificial neural network to typhoon rainfall forecasting. Hydrological Processes 19 9 (2005) 1825-1837
11. Lin G.F., and Chen G.R. A systematic approach to the input determination for neural network rainfall-runoff models. Hydrological Processes 22 14 (2008) 2524-2530
12. Liong S.Y., and Sivapragasam C. Flood stage forecasting with support vector machines. Journal of the American Water Resources Association 38 1 (2002) 173-186
13. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modeling and Software 15 (2000) 101-124
14. Pan T.Y., and Wang R.Y. State space neural networks for short term rainfall-runoff forecasting. Journal of Hydrology 297 1-4 (2004) 34-50
15. Sivapragasam C., and Liong S.Y. Flow categorization model for improving forecasting. Nordic Hydrology 36 1 (2005) 37-48
16. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
17. Vapnik V. Statistical Learning Theory (1998), John Wiley, New York
18. Xu Z.X., and Li J.Y. Short-term inflow forecasting using an artificial neural network model. Hydrological Processes 16 12 (2002) 2423-2439
19. Yu P.S., and Chen S.T. Updating real-time flood forecasting using a fuzzy rule-based model. Hydrological Sciences Journal 50 2 (2005) 265-278
20. Yu X.Y., and Liong S.Y. Forecasting of hydrologic time series with ridge regression in feature space. Journal of Hydrology 332 3-4 (2007) 290-302
21. 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