Abstract
Forecasting reservoir inflow is important to hydropower reservoir management and scheduling. An Adaptive-Network-based Fuzzy Inference System (ANFIS) is successfully developed to forecast the long-term discharges in Manwan Hydropower. Using the long-term observations of discharges of monthly river flow discharges during 1953-2003, different types of membership functions and antecedent input flows associated with ANFIS model are tested. When compared to the ANN model, the ANFIS model has shown a significant forecast improvement. The training and validation results show that the ANFIS model is an effective algorithm to forecast the long-term discharges in Manwan Hydropower. The ANFIS model is finally employed in the advanced water resource project of Yunnan Power Group.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Box, G.E.P., Jenkins, G.M.: Time Series Analysis Forecasting and Control. Holden-Day, San Francisco (1976)
ASCE Task Committee: Artificial neural networks in hydrology-I: Preliminary concepts. Journal of Hydrologic Engineering, ASCE 5(2), 115–123 (2000)
ASCE Task Committee: Artificial neural networks in hydrology-II: Hydrological applications. Journal of Hydrologic Engineering, ASCE 5(2), 124–137 (2000)
Jang, J.-S.R.: Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm. In: Proc. of the Ninth National Conf. on Artificial Intelligence, pp. 762–767 (1991)
Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)
Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy modeling and control. Proceedings of the IEEE 83(3), 378–406 (1995)
Jang, J.-S.R., Sun, C.-T.: Neuro-Fuzzy and Soft Computing. In: A Computational Approach to Learning and Machine Intelligence (1997)
Lo, S.-P.: An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology 142, 665–675 (2003)
Sahooa, G.B., Raya, C., Wadeb, H.F.: Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks. Ecological Modelling 183, 29–46 (2005)
Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Zopounidis, C.D.: Development of dynamic cognitive networks as complex systems approximators: validation in financial time series. Applied Soft Computing 5, 157–179 (2005)
Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S.: A neuro-fuzzy computing technique for modeling hydrological times series. Journal of Hydrology 291, 52–66 (2004)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)
Sugeno, M., Kang, G.T.: Structure identification of fuzzy model: fuzzy Sets and Systems 28, 15–33 (1988)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cysbernetics 15, 116–132 (1985)
Tsukamoto, Y.: An approach to fuzzy reasoning method. Advances in fuzzy set theory and applications, 137–149 (1979)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Masters, T.: Practical Neural Networks Recipes C++. Academic Press, San Diego (1993)
Fitch, J.P., Lehman, S.K., Dowla, F.U., Lu, S.K., Johansson, E.M., Goodman, D.M.: Ship Wake Detection Procedure Using Conjugate Gradient Trained Artificial Neural Network. IEEE Transactions on Geosciences and Remote Sensing 9(5), 718–725 (1991)
Moller, M.F.: A Scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 523–533 (1993)
Huang, W., Foo, S.: Neural network modeling of salinity in Apalachicola River. Water Resources Research 31, 2517–2530 (2002)
Vernieuwe, H., Georgieva, O., De Baets, B., Pauwels, V.R.N., Verhoest, N.E.C., De Troch, F.P.: Comparison of data-driven Takagi–Sugeno models of rainfall–discharge dynamics. Journal of Hydrology 302, 173–186 (2005)
Zadeh, L.A.: Fuzzy sets. Information 8(3), 338–353 (1965)
Zadeh, L.A.: Fuzzy Logic. Computer 1(4), 83–93 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, CT., Lin, JY., Sun, YG., Chau, K. (2005). Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_145
Download citation
DOI: https://doi.org/10.1007/11539902_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)