Fuzzy Neural Network Model for Hydrologic Flow Routing
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 4
Abstract
This paper presents a new approach to river flow prediction using a fuzzy neural network (FNN) model. An FNN combines the learning ability of artificial neural networks with the merits of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The model displays the stored knowledge in terms of fuzzy linguistic rules, which allows the model decision-making process to be examined and understood in detail. The FNN model is tested on the river Brahmaputra using flow data at various gauged sites in India. The advantages of using the FNN model in river flow prediction are discussed using the case study.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgment
The writers wish to acknowledge the support given by the Assam State Flood Control Department by providing the necessary data for the analysis.
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrologic Eng., 5(2), 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrologic Eng., 5(2), 124–137.
Bowden, G. J., Maier, H. R., and Dandy, G. C. (2002). “Optimal division of data for neural network models in water resources applications.” Water Resour. Res., 38(2), 2-1–2-11.
Brown, M., and Harris, C. (1994). Neuro-fuzzy adaptive modeling and control. Prentice-Hall, Englewood Cliffs, N.J.
Brown, M., and Harris, C. (1995). “A perspective and critique of adaptive neurofuzzy systems used for modeling and control applications.” Int. J. Neural Syst., 6(2), 197–220.
Chandramouli, V., and Raman, H. (2001). “Multireservoir modeling with dynamic programming and neural networks.” J. Water Resour. Plan. Manage., 127(2), 89–98.
Chung, F. L., and Duan, J. C. (2000). “Multistage fuzzy neural network modeling.” IEEE Trans. Fuzzy Syst., 8(2), 125–142.
Cox, E. (1994). The fuzzy systems hand book, Academic, San Diego.
Dawson, C. W., and Wilby, R. (1998). “An artificial neural network approach to rainfall-runoff modeling.” Hydrol. Sci. J., 43(1), 47–66.
French, M. N., Krajewski, W. F., and Cuykendall, R. R. (1992). “Rainfall forecasting in space and time using a neural network.” J. Hydrol., 137(1), 1–31.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.” J. Comput. Civ. Eng., 8(2), 201–220.
Liong, S.-Y., Lim, W.-H., and Paudyal, G. N. (2000). “River stage forecasting in Bangladesh: Neural network approach.” J. Comput. Civ. Eng., 14(1), 1–8.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environmental Modeling and Software, 15(1), 101–123.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall-runoff models.” Hydrol. Sci. J., 41(3), 399–418.
Mitra, S., and Hayashi, Y. (2000). “Neuro-fuzzy rule generation: Survey in soft computing framework.” IEEE Trans. Neural Netw., 11(3), 748–768.
Russell, S. O., and Campbell, P. F. (1996). “Reservoir operating rules with fuzzy programming.” J. Water Resour. Plan. Manage., 122(3), 165–170.
Sayed, T., and Razavi, A. (2000). “Comparison of neural and conventional approaches to mode choice analysis.” J. Comput. Civ. Eng., 14(1), 23–30.
Shrestha, B. P., Duckstein, L., and Stakhiv, E. Z. (1996). “Fuzzy rule-based modeling of reservoir operation.” J. Water Resour. Plan. Manage., 122(4), 262–269.
Thirumalaiah, K., and Deo, M. C. (1998). “River stage forecasting using artificial neural networks.” J. Hydrologic Eng., 3(1), 26–32.
Thirumalaiah, K., and Deo, M. C. (2000). “Hydrological forecasting using neural networks.” J. Hydrologic Eng., 5(2), 180–189.
Wang, L. X., and Mendel, J. M. (1992). “Fuzzy basis functions, universal approximation, and orthogonal least squares learning.” IEEE Trans. Neural Netw., 3(5), 807–814.
Zadeh, L. A. (1965). “Fuzzy sets.” Information and Control, 8, 338–353.
Zadeh, L. A. (1973). “Outline of a new approach to the analysis of complex systems and decision processes.” IEEE Trans. Syst. Man Cybern., 3, 28–44.
Zhang, B., and Govindaraju, R. S. (2000). “Prediction of watershed runoff using Bayesian concepts and modular neural networks.” Water Resour. Res., 36(3), 753–762.
Information & Authors
Information
Published In
Copyright
© 2005 ASCE.
History
Received: Sep 9, 2002
Accepted: Aug 12, 2004
Published online: Jul 1, 2005
Published in print: Jul 2005
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.