[Abbott, M.B., Refsgaard, J.C., 1996. Distributed Hydrological Modeling. Kluver Academic Publishers, Dordrecht, 17-39.10.1007/978-94-009-0257-2]Search in Google Scholar
[Anctil, F., Tape, D.G., 2004. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J. Environmental Engng. and Science, 3, 21- -128.10.1139/s03-071]Search in Google Scholar
[Bray, M., Han, D., 2004. Identification of support vector machines for runoff modelling. J. Hydroinformatics, 6, 265- -280.10.2166/hydro.2004.0020]Search in Google Scholar
[Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with a neural network model. Water Resour. Res., 35, 1191-1197.10.1029/1998WR900086]Search in Google Scholar
[Cengiz, T.M., 2011. Periodic structures of great lakes levels using wavelet analysis. J. Hydrol. Hydromech., 59, 1, 24-35.10.2478/v10098-011-0002-z]Search in Google Scholar
[Cigizoglu, H.K., 2005. Generalized regression neural networks in monthly flow forecasting. Civil Engineering and Environmental Systems, 22, 2, 71-84.10.1080/10286600500126256]Search in Google Scholar
[Cimen, M., 2008. Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal, 53, 3, 656-666.10.1623/hysj.53.3.656]Search in Google Scholar
[Coulibaly, P., Anctil, F., Bobee, B., 2000. Daily reservoir inflow forecasting using artificial neural Networks with stopped training approach. J. Hydrol., 230, 244-257.10.1016/S0022-1694(00)00214-6]Search in Google Scholar
[Daubechies, I., 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36, 5, 961-1005.10.1109/18.57199]Search in Google Scholar
[Hagan, M.T., Menhaj, M.B., 1994. Training feed forward techniques with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 6, 989-993.10.1109/72.32969718267874]Search in Google Scholar
[Ham, F., Kostanic, I., 2001. Principles of Neurocomputing for Science and Engineering. Macgraw-Hill. USA.]Search in Google Scholar
[Kisi, O., Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J. Hydrol., 399, 132-140.10.1016/j.jhydrol.2010.12.041]Search in Google Scholar
[Kucuk, M., Agiralioglu, N., 2006, Wavelet regression technique for streamflow prediction. J. Applied Statistics, 33, 9, 943-960.10.1080/02664760600744298]Search in Google Scholar
[Lin, J.Y., Cheng, C.T., Chau, K.W., 2006. Using support vector machines for long-term discharge prediction. Hydrological Sciences J., 51, 4, 599-612.10.1623/hysj.51.4.599]Search in Google Scholar
[Liong, S.Y., Sivapragasam, C., 2002. Flood stage forecasting with support vector machines. J. Amer. Water Resources Assoc., 38, 1, 173-186.10.1111/j.1752-1688.2002.tb01544.x]Search in Google Scholar
[Mallat, S.G., 1989. A theory for multi resolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674-693.10.1109/34.192463]Search in Google Scholar
[Mallows, C.L., 1973. Some comments on Cp. Technometrics, 15, 4, 661-675.10.1080/00401706.1973.10489103]Search in Google Scholar
[Mercer, J., 1909. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society, London, 209, 415-446.10.1098/rsta.1909.0016]Search in Google Scholar
[Okkan, U., 2011. Application of Levenberg-Marquardt optimization algorithm based multilayer neural networks for hydrological time series modeling. An Int. J. Optimization and Control: Theories & Applications, 1, 1, 53-63.10.11121/ijocta.01.2011.0038]Search in Google Scholar
[Okkan, U., 2012. Performance of least squares support vector machine for monthly reservoir inflow prediction. Fresenius Environmental Bull., 21, 3, 611-620.]Search in Google Scholar
[Rajaee, T., Nourani, V., Mohammad, Z.K., Kisi, O., 2011. River suspended sediment load prediction: Application of ANN and wavelet conjunction model. J. Hydrol. Engng, 16, 8, 613-627.10.1061/(ASCE)HE.1943-5584.0000347]Search in Google Scholar
[Razavi, S., Araghinejad, S., 2009. Reservoir inflow modeling using temporal neural networks with forgetting factor approach. Water Resour. Management, 23, 39-55.10.1007/s11269-008-9263-7]Search in Google Scholar
[Salas, J.D., Delleur, J.W., Yevjevich, V., Lane, W.L., 1980. Applied modeling of hydrologic time series. Water Resouces Pub., p. 484.]Search in Google Scholar
[Sudheer, K.P., Gosain, A.K., Ramasastri, K.S., 2002, A datadriven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Processes, 16, 1325-1330.10.1002/hyp.554]Search in Google Scholar
[Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., 2002. Least Squares Support Vector Machines. World Science, Singapore.10.1142/5089]Search in Google Scholar
[Vapnik, V., 1998. Statistical Learning Theory. John Wiley & Sons, Toronto.]Search in Google Scholar
[Wang, W., Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1, 67-71.]Search in Google Scholar
[Wang, W., van Gelder, P., Vrijling, J.K., Ma, J., 2006. Forecasting daily streamflow using hybrid ANN models. J. Hydrol., 324, 383-399.10.1016/j.jhydrol.2005.09.032]Search in Google Scholar
[Wang, W., Jin, J., Li, Y., 2009. Prediction of inflow at Three Gorges Dam in Yangtze River with wavelet network model. Water Resour. Management, 23, 2791-2803.10.1007/s11269-009-9409-2]Search in Google Scholar
[Wu, C.L., Chau, K.W., Li, Y.S., 2008. Predicting monthly streamflow using data-driven models coupled with datapreprocessing techniques. Water Resour. Res., 45, 8, 1-23.10.1029/2007WR006737]Search in Google Scholar