The combined use of wavelet transform and black box models in reservoir inflow modeling
By: Umut Okkan and Zafer Ali Serbes
Open Access
|Jun 2013References
- Abbott, M.B., Refsgaard, J.C., 1996. Distributed Hydrological Modeling. Kluver Academic Publishers, Dordrecht, 17-39.
- 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.
- Bray, M., Han, D., 2004. Identification of support vector machines for runoff modelling. J. Hydroinformatics, 6, 265- -280.
- Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with a neural network model. Water Resour. Res., 35, 1191-1197.
- Cengiz, T.M., 2011. Periodic structures of great lakes levels using wavelet analysis. J. Hydrol. Hydromech., 59, 1, 24-35.
- Cigizoglu, H.K., 2005. Generalized regression neural networks in monthly flow forecasting. Civil Engineering and Environmental Systems, 22, 2, 71-84.
- Cimen, M., 2008. Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal, 53, 3, 656-666.
- Coulibaly, P., Anctil, F., Bobee, B., 2000. Daily reservoir inflow forecasting using artificial neural Networks with stopped training approach. J. Hydrol., 230, 244-257.
- Daubechies, I., 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36, 5, 961-1005.
- Hagan, M.T., Menhaj, M.B., 1994. Training feed forward techniques with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 6, 989-993.
- Ham, F., Kostanic, I., 2001. Principles of Neurocomputing for Science and Engineering. Macgraw-Hill. USA.
- Kisi, O., Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J. Hydrol., 399, 132-140.
- Kucuk, M., Agiralioglu, N., 2006, Wavelet regression technique for streamflow prediction. J. Applied Statistics, 33, 9, 943-960.
- 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.
- Liong, S.Y., Sivapragasam, C., 2002. Flood stage forecasting with support vector machines. J. Amer. Water Resources Assoc., 38, 1, 173-186.
- 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.
- Mallows, C.L., 1973. Some comments on Cp. Technometrics, 15, 4, 661-675.
- 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.
- 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 & Applications1, 1, 53-63.
- Okkan, U., 2012. Performance of least squares support vector machine for monthly reservoir inflow prediction. Fresenius Environmental Bull., 21, 3, 611-620.
- 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.
- Razavi, S., Araghinejad, S., 2009. Reservoir inflow modeling using temporal neural networks with forgetting factor approach. Water Resour. Management, 23, 39-55.
- Salas, J.D., Delleur, J.W., Yevjevich, V., Lane, W.L., 1980. Applied modeling of hydrologic time series. Water Resouces Pub., p. 484.
- 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.
- Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., 2002. Least Squares Support Vector Machines. World ScienceSingapore.
- Vapnik, V., 1998. Statistical Learning Theory. John Wiley & Sons, Toronto.
- Wang, W., Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1, 67-71.
- Wang, W., van Gelder, P., Vrijling, J.K., Ma, J., 2006. Forecasting daily streamflow using hybrid ANN models. J. Hydrol., 324, 383-399.
- 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.
- 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.
DOI: https://doi.org/10.2478/johh-2013-0015 | Journal eISSN: 1338-4333 (formerly 0042-790X) | Journal ISSN: 0042-790X
Language: English
Page range: 112 - 119
Published on: Jun 1, 2013
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Keywords:
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© 2013 Umut Okkan, Zafer Ali Serbes, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons License.