Use of radial basis function type artificial neural networks for runoff simulation

Computer-Aided Civil and Infrastructure Engineering

Volumn 13, Issue 2, 1998, Pages 91-99

  Jayawardena, A W ^a   Achela, D ^a   Fernando, K ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DISCHARGE (FLUID MECHANICS); HYDROLOGY; NEURAL NETWORKS; PARAMETER ESTIMATION;

RADIAL BASIS FUNCTION (RBF);

RUNOFF;

MODELING; NEURAL NETWORK; RUNOFF;

EID: 0032030147     PISSN: 10939687     EISSN: None     Source Type: Journal    
DOI: 10.1111/0885-9507.00089     Document Type: Article

References (19)

1. Abdalla, K. M. & Stavroulakis, G. E., A backpropagation neural network model for semi-rigid steel connections, Microcomputers in Civil Engineering, 10 (1995), 77-87.
2. Bianchini, M., Frasconi, P. & Gori, M., Learning without local minima in radial basis functions networks, IEEE Transactions in Neural Networks, 6 (3) (1995), 749-55.
3. Chen, S., Cowan, C. F. N. & Grant, P. M., Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, 2(2) (1991), 302-9.
4. Cote, M., Grandjean, B. P. A., Lessasrd, P. & Thibault, J., Dynamic modeling of the activated sludge process: Improving prediction using neural networks, Water Research, 29(4) (1995), 995-1004.
5. Goh, A. T. C., Seismic liquefaction potential assessed by neural networks, Journal of Geotechnical Engineering, ASCE, 120(9) (1994), 1467-80.
6. Grubert, J. P., Application of neural networks in stratified flow stability analysis. Journal of Hydraulic Engineering, ASCE, 121(7) (1995), 523-32.
7. Hush, D. R. & Home, B. G., Progress in supervised neural networks: What's new since Lippmann? IEEE Signal Processing magazine (January 1993), 8-39.
8. Isobe, I., Ohkado, T., Hanyuda, H., et al., The development of a forecasting system of the water levels of rivers by neural networks (in Japanese), Journal of Japan Society of Hydrology and Water Resources, 7(2) (1994), 90-7.
9. Jayawardena, A. W. & Fernando. D. A. K., Hydrological fore-casting using artificial neural networks, in Proceedings of the Second International Study Conference on GEWEX in Asia and GAME, Thailand, 1995, pp. 376-9.
10. Jayawardena, A. W. & Fernando, D. A. K., Artificial neural networks in hydrometeorological modelling, in Proceedings of the Fourth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering: Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering, B. H. V. Topping (ed.), Civil Comp Press, Cambridge, UK, 1995, pp. 115-20.
11. Karunanithi, N., Grenny, W. J., Whitley, D. & Bovee K., Neural networks for river flow prediction, Journal of Computing in Civil Engineering, ASCE, 8(2) (1994), 201-20.
12. Liong, S. Y. & Chan, W. T., Runoff volume estimates with neural networks, in Proceedings of the Third International Conference in the Application of Artificial Intelligence to Civil and Structural Engineering: Neural Networks and Combinatorial Optimization in Civil and Structural Engineering, B. H. V. Topping & A. I. Khan (eds.), Civil Comp Press, Edinburgh, 1993, pp. 67-70.
13. Moody, J. & Darken, C. J., Fast learning in networks of locally tuned processing units, Neural Computation, 1(2) (1989), 281-94.
14. Park, J. & Sandberg, I. W., Universal approximations using radial-basis-function networks, Neural Computation, 3(2) (1991), 246-57.
15. Raman, H. & Sunilkumar, N., Modelling water resources time series using artificial neural networks, Hydrological Sciences Journal, 40(2) (1995), 145-62.
16. Smith, J. & Eli, R. N., Neural-network models of rainfall-runoff process, Journal of Water Resources Planning and Management, 121(6) (1995), 499-508.
17. Specht, D. F., A general regression neural network, IEEE Transactions on Neural Networks, 2(6) (1991), 568-76.
18. Tau, J. T. & Gonzalez, R. C., Pattern Recognition Principles, Addison-Wesley, Reading, MA, 1974.
19. Wasserman. P. D., Advanced Methods in Neural Computing. Van Nostrand Reinhold, New York, 1993, pp. 147-76.