Modeling river stage-discharge relationships using different neural network computing techniques

Clean - Soil, Air, Water

Volumn 37, Issue 2, 2009, Pages 160-169

  Kisi, Özgür ^a   Çobaner, Murat ^a  

^a ERCIYES UNIVERSITY   (Turkey)

Author keywords

Neural networks; Rating curve; Regression; Stage discharge relationship

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; NONLINEARITY; NUMERICAL MODEL; PARALLEL COMPUTING; RATING CURVE; REGRESSION ANALYSIS; RIVER DISCHARGE;

EURASIA; TURKEY;

EID: 67649845504     PISSN: 18630650     EISSN: None     Source Type: Journal    
DOI: 10.1002/clen.200800010     Document Type: Article

References (34)

1. J. Smith, R. N. Eli, Neural network models of rainfall-runoff process, J. Water Resour. Planning Manage. 1995, 499-508.
2. A. S. Tokar, P. A. Johnson, Rainfall-runoff modeling using artificial neural networks, J. Hydrol. Eng., ASCE 1999, 4 (3), 232-239.
3. D. P. Solomatine, K. N. Dulal, Model trees as an alternative to neural networks in rainfall-runoff modeling, Hydrol. Sci. J. 2003, 48 (3), 399-411.
4. G. F. Lin, L. H. Chen, A nonlinear rainfall-runoff model using radial basis function network, J. Hydrol. 2004, 289, 1-8.
5. H. Raman, N. Sunilkumar, Multivariate modeling of water resources time series using artificial neural networks, Hydrol. Sci. J. 1995, 40 (2), 145-163.
6. T. A. Clair, J. M. Ehrman, Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers, Water Resour. Res. 1998, 34 (3), 447-455.
7. R. Chibanga, J. Berlamont, J. Vandewalle, Modeling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin. Hydrol. Sci. J. 2003, 48 (3), 363-379.
8. H. K. Cigizoglu, Estimation, forecasting and extrapolation of river flows by artificial neural networks, Hydrol. Sci. J. 2003, 48 (3), 349-361.
9. O. Kisi, River flow modeling using artificial neural networks, J. Hydrol. Eng., ASCE 2004, 9 (1), 60-63.
10. H. K. Cigizoglu, O. Kisi, Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data, Nordic Hydrol. 2005, 36 (1), 49-64.
11. G. Tayfur, Artificial neural networks for sheet sediment transport, Hydrol. Sci. J. 2002, 47 (6), 879-892.
12. Kisi, O. (2004b) Multi-layer perceptrons with Levenberg-Marquardt optimization algorithm for suspended sediment concentration prediction and estimation, Hydrol. Sci. J. 2004, 49 (6), 1025-1040.
13. O. Kisi, Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydrol. Sci. J. 2005, 50 (4), 683-696.
14. M. Saad, P. Bigras, A. Turgeon, R. Duquette, Fuzzy learning decomposition for the scheduling of hydroelectric power systems, Water Resour. Res. 1996, 32 (1), 179-186.
15. P. Coulibaly, F. Anctil, B. Bobe'e, Real time neural network based forecasting system for hydropower reservoirs, in Proc. of the First Int. Conf. on New Information Technologies for Decision Making in Civil Engineering (Ed: E. T. Miresco). University of Quebec, Montreal, Canada, October 1998, pp. 1001-1011.
16. S. K. Jain, D. Das, D. K. Srivastava, Application of ANN for reservoir inflow prediction and operation, J. Water Resour. Planning Manage., ASCE 1999, 125 (5), 263-271.
17. P. Coulibaly, M. Hache, V. Fortin, B. & Bobe'e, Improving daily reservoir inflow forecasting with model combination, J. Hydrol. Eng., ASCE 2005, 10 (2), 91-99.
18. S. K. Jain, D. Chalisgaonkar, Setting up stage discharge relations using ANN, J. Hydrol. Eng., ASCE 2000, 5 (4), 428-433.
19. B. Bhattacharya, D. P. Solomatine, Application of artificial neural network in stage-discharge relationships, 4th Int. Conf. on Hydroinformatics. Cedar-Rapids, USA, July 2000.
20. P. Deka, V. Chandramouli, A fuzzy neural network model for deriving the river stage-discharge relationship. Hydrol. Sci. J. 2003, 48 (2), 197-209.
21. B. Bhattacharya, D. P. Solomatine, Neural networks and M5 model trees in modeling water level-discharge relationship, Neurocomputing 2005, 63, 381-396.
22. K. P. Sudheer, S. K. Jain, Radial basis function neural networks for modeling rating curves, J. Hydrol. Eng., ASCE 2003, 8 (3), 161-164.
23. S. Supharatid, Application of a neural network model in establishing a stage-discharge relationship for a tidal river, Hydrol. Processes 2003, 17, 3085-3099.
24. M. Bayazit, B. Oguz, Probability and Statistics for Engineers, Birsen Publishing House, Istanbul, Turkey 1998, p. 159.
25. R. Lippman, An introduction to computing with neural nets, IEEE ASSP Mag. 1987, 4, 4-22.
26. D. Marquardt, An algorithm for least squares estimation of nonlinear parameters, J. Soc. Ind. Appl. Math. 1963, 11, 431-441.
27. M. T. Hagan, M. B. Menhaj, Training feed forward networks with the Marquaradt algorithm, IEEE Trans. Neural Networks 1994, 6, 861-867.
28. R. D. Reed, R. J. Marks, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, The MIT Press, Cambridge, MA 1998, p.174.
29. L. E. K. Achenie, Computational experience with a quasi Newton based training of the feedforward neural network, In World Congress on Neural Networks Vol. 3, Lawrence Erlbaum, Hillsdale, NJ 1994, pp. 607-612.
30. D. Broomhead, D. Lowe, Multivariable functional interpolation and adaptive networks, Complex Syst. 1988, 2, 321-355.
31. G. C. Lee, S. H. Chang, Radial basis function networks applied to DNBR calculation in digital core protection systems, Ann. Nucl. Energy 2003, 30, 1561-1572.
32. K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 1989, 2, 359-366.
33. W. C. Dawson, R. Wilby, An artificial neural network approach to rainfall-runoff modeling, Hydrol. Sci. J. 1998, 43 (1), 47-66.
34. ASCE Task Committee, Artificial neural networks in hydrology, II: Hydrological applications, ASCE J. Hydrol. Eng. 2000, 5 (2), 124-137.