REFERENCES

• ASCE Task Committee . 2000a . Artificial neural networks in hydrology. I: Preliminary concepts. . J. Hydrol. Engng ASCE , 5 ( 2 ) : 115 – 123 .
   Google Scholar
• ASCE Task Committee . 2000b . Artificial neural networks in hydrology. II: Hydrological applications. . J. Hydrol. Engng ASCE , 5 ( 2 ) : 124 – 137 .
   Web of Science ®Google Scholar
• Asefa , T. , Kemblowski , M. W. , Urroz , G. , McKee , M. and Khalil , A. 2004 . Support vectors-based groundwater head observation networks design. . Water Resour. Res. , 40 : W11509 doi:10.1029/2004WR003304
   Google Scholar
• Box , G. E. P. and Jenkins , G. M. 1976 . Time Series Analysis, Forecasting and Control San Francisco, California, USA : Holden-Day .
   Google Scholar
• Bray , M. and Han , D. 2004 . Identification of support vector machines for runoff modelling. . J. Hydroinf , 6 ( 4 ) : 265 – 280 .
   Web of Science ®Google Scholar
• Burges , C. J. C. 1998 . A tutorial on support vector machines for pattern recognition. . Data Mining and Knowledge Discovery , 2 : 121 – 167 .
   Web of Science ®Google Scholar
• Cao , L. J. and Tay , F. E. H. 2003 . Support vector machine with adaptive parameters in financial time series forecasting. . IEEE Trans. Neural Networks , 14 ( 6 ) : 1506 – 1518 .
   PubMed Web of Science ®Google Scholar
• Chen , P. W. , Wang , J. Y. and Lee , H. M. 2004 . “ Model selection of SVMs using GA approach. ” . In 2004 IEEE International Joint Conference on Neural Networks Vol. 3 , 2035 – 2040 . New York, USA : IEEE .
   Google Scholar
• Campolo , M. , Soldati , A. and Andreussi , P. 2003 . Artificial neural network approach to flood forecasting in the River Arno. . Hydrol. Sci. J. , 48 ( 3 ) : 381 – 398 .
   Web of Science ®Google Scholar
• Cigizoglu , H. K. 2003 . Estimation, forecasting and extrapolation of river flows by artificial neural networks. . Hydrol. Sci. J. , 48 ( 3 ) : 349 – 362 .
   Web of Science ®Google Scholar
• Dawson , C. W. and Wilby , R. 1998 . An artificial neural network approach to rainfall—runoff modelling. . Hydrol. Sci. J. , 43 ( 1 ) : 47 – 66 .
   Web of Science ®Google Scholar
• Dong , B. , Cao , C. and Lee , S. E. 2005 . Applying support vector machines to predict building energy consumption in tropical region. . Energy and Buildings , 37 : 545 – 553 .
   Web of Science ®Google Scholar
• Duan , Q. Y. , Gupta , V. K. and Sorooshian , S. 1993 . Shuffled complex evolution approach for effective and efficient minimization. . J. Optim. Theory Appl. , 76 ( 3 ) : 501 – 521 .
   Web of Science ®Google Scholar
• Duan , Q. Y. , Sorooshian , S. and Gupta , V. K. 1992 . Effective and efficient global optimization for conceptual rainfall—runoff models. . Water Resour. Res. , 28 ( 4 ) : 1015 – 1031 .
   Web of Science ®Google Scholar
• Duan , Q. Y. , Sorooshian , S. and Gupta , V. K. 1994 . Optimal use of the SCE-UA global optimization method for calibrating watershed models. . J. Hydraul. Engng , 158 ( 1 ) : 265 – 284 .
   Google Scholar
• Fine , S. and Scheinberg , K. 2001 . Efficient SVM training using low-rank kernel representation. . J. Machine Learning Res. , 2 : 243 – 264 .
   Web of Science ®Google Scholar
• Giustolisi , O. and Laucelli , D. 2005 . Improving generalization of artificial neural networks in rainfall—runoff modeling. . Hydrol. Sci. J. , 50 ( 3 ) : 439 – 457 .
   Web of Science ®Google Scholar
• Gunn , S. R. 1998 . Support vector machines for classification and regression , ISIS Tech. Report. ISIS-1–98 Southampton, UK : Image Speech Intelligent System Research Group, University of Southampton .
   Google Scholar
• Holland , J. 1992 . Adaptation in Natural and Artificial Systems Cambridge, Massachusetts, USA : second edn. MIT Press .
   Google Scholar
• Hsu , C. W. , Chang , C. C. and Lin , C. J. 2003 . A practical guide to support vector classification. Tech. Report, Dept Computer Sci. & Info Taiwan, China : Engng, National Taiwan University .
   Google Scholar
• Hu , T. S. , Lam , K. C. and Ng , S. T. 2001 . River flow time series prediction with range-dependent neural network. . Hydrol. Sci. J. , 46 ( 5 ) : 729 – 746 .
   Web of Science ®Google Scholar
• Hu , T. S. , Lam , K. C. and Ng , S. T. 2005 . A modified neural network for improving river flow prediction. . Hydrol. Sci. J. , 50 ( 2 ) : 299 – 318 .
   Web of Science ®Google Scholar
• Joachims , T. 1999 . “ Making large-scale support vector machines learning practical. ” . In Advances in Kernel Methods—Support Vector Learning Edited by: Schölkopf , B. , Burges , C. J. C. and Smola , A. J. 169 – 184 . Cambridge, Massachusetts, USA : MIT Press .
   Google Scholar
• Keerthi , S. S. and Lin , C. J. 2001 . Asymptotic behaviors of support vector machines with Gaussian kernel. . Neural Computation , 15 ( 7 ) : 1667 – 1689 .
   Web of Science ®Google Scholar
• Lin , H. T. and Lin , C. J. 2003 . A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Tech. Report, Dept of Computer Sci. & Info Taiwan, China : Engng, National Taiwan University .
   Google Scholar
• Liong , S. Y. and Sivapragasm , C. 2002 . Flood stage forecasting with SVM. . J. Am. Water Res. Assoc. , 38 ( 1 ) : 173 – 186 .
   Web of Science ®Google Scholar
• Moller , M. F. 1993 . A scaled conjugate gradient algorithm for fast supervised learning. . Neural Networks , 6 : 523 – 533 .
   Web of Science ®Google Scholar
• Nelder , J. A. and Mead , R. 1965 . A simplex method for function minimization. . Comput. J. , 7 ( 4 ) : 308 – 313 .
   Web of Science ®Google Scholar
• Nunoo , C. and Mrawira , D. 2004 . Shuffled complex evolution algorithms in infrastructure works programming. . J. Comput. Civil Engng , 18 ( 3 ) : 257 – 266 .
   Web of Science ®Google Scholar
• Pai , P. F. and Hong , W. C. 2005 . Support vector machines with simulated annealing algorithms in electricity load forecasting. . Energy Conversion and Manage , 46 ( 17 ) : 2669 – 2688 .
   Google Scholar
• Platt , J. 1999 . “ Flat training of support vector machines using sequential minimal optimisation. ” . In Advances in Kernel Methods—Support Vector Learning Edited by: Schölkopf , B. , Burges , C. J. C. and Smola , A. J. 185 – 208 . Cambridge, Massachusetts, USA : MIT Press .
   Google Scholar
• Price , W. L. 1987 . Global optimization algorithm for a CAD workstation. . J. Osptim. Theory Appl. , 55 ( 1 ) : 133 – 146 .
   Web of Science ®Google Scholar
• Sarle , W. S. 1997 . Neural network FAQ. Periodic posting to the Usenet news group comp.ai.neural-nets Available at: ftp://ftp.sas.com/pub/neural/FAQ.html.
   Google Scholar
• See , L. and Openshaw , S. 2000 . A hybrid multi-model approach to river level forecasting. . Hydrol. Sci. J. , 45 ( 4 ) : 523 – 536 .
   Web of Science ®Google Scholar
• Smola , A. J. and Schölkopf , B. 2004 . A tutorial on support vector regression. . Statist. and Comput. , 14 : 199 – 222 .
   Web of Science ®Google Scholar
• Sudheer , K. P. , Gosain , A. K. and Ramasastri , K. S. 2002 . A data-driven algorithm for constructing artificial neural network rainfall—runoff models. . Hydrol. Processes , 16 : 1325 – 1330 .
   Web of Science ®Google Scholar
• Sy , N. L. 2006 . Modelling the infiltration process with a multi-layer perceptron artificial neural network. . Hydrol. Sci. J. , 51 ( 1 ) : 3 – 20 .
   Web of Science ®Google Scholar
• 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. . Chemometrics and Intelligent Laboratory Systems , 69 : 35 – 49 .
   Web of Science ®Google Scholar
• Tokar , A. S. and Johnson , P. A. 1999 . Rainfall—runoff modeling using artificial neural network. . J. Hydrol. Engng ASCE , 4 ( 3 ) : 232 – 239 .
   Web of Science ®Google Scholar
• Vapnik , V. 1998 . Statistical Learning Theory New York, USA : Springer .
   Google Scholar
• Vapnik , V. , Golowich , S. and Smola , A. J. 1997 . Support vector method for function approximation, regression estimation, and signal processing. . Adv. Neural Info. Processing Systems , 9 : 281 – 287 .
   Google Scholar
• Wilby , R. L. , Abrahart , R. J. and Dawson , C. W. 2003 . Detection of conceptual model rainfall—runoff processes inside an artificial neural network. . Hydrol. Sci. J. , 48 ( 2 ) : 163 – 181 .
   Web of Science ®Google Scholar
• Williams , C. K. I. and Seeger , M. 2001 . “ Using the Nyström method to speed up kernel machines. ” . In Advances in Neural Information Processing Systems Edited by: Leen , T. K. , Dietterich , T. G. S. and Tresp , V. Vol. 13 , 682 – 688 . Cambridge, Massachusetts, USA : MIT Press .
   Google Scholar
• Yu , X. Y. , Liong , S. Y. and Babovic , V. 2004 . EC-SVM approach for real-time hydrologic forecasting. . J. Hydroinf. , 6 ( 3 ) : 209 – 233 .
   Web of Science ®Google Scholar