Performance of neural network models with Kalman learning rule for flow routing in a river system

Fresenius Environmental Bulletin

Volumn 16, Issue 11 B, 2007, Pages 1474-1484

  Diamantopoulou, Maria J ^a   Georgiou, Pantazis E ^a   Papamichail, Dimitris M ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Artificial neural networks; Backpropagation; Cascade correlation; Kalman learning rule; River flow routing; Time lagged daily flows

Indexed keywords

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; KALMAN FILTER; LEARNING; MODELING; PERFORMANCE ASSESSMENT; RESERVOIR; RIVER FLOW; ROUTING; WATER MANAGEMENT;

ALIAKMON RIVER; EURASIA; EUROPE; GREECE; SOUTHERN EUROPE;

EID: 38049027371     PISSN: 10184619     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (42)

1. National Research Council (2000) Risk analysis and uncertainty in flood damage reduction studies. National Academy of Sciences.
2. Maidment, D.R. (1993) Handbook of hydrology. McGraw-Hill Company, USA.
3. Hipel, K.W. and McLeod, A.I. (1994) Time series modelling of water resources and environmental systems. Elsevier Science B.V., Development in Water Science, No. 45.
4. Papamichail, D.M. and Georgiou, P.E. (2001) Seasonal ARIMA inflow models for reservoir sizing. Journal of American Water Resources Association, 37, 877-885.
5. 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, 101-124.
6. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in Hydrlogy. I. Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115-123.
7. Karunanithi, N., Grenmey, W.J., Whitley, D. and Bovee, K. (1994) Neural networks for river flow forecasting. Journal of Computing and Civil Engineering, 8, 201-218.
8. Maier, H.R. and Dandy, G.C. (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research, 32, 1013-1022.
9. Minns, A.W. and Hall, M.J. (1996) Artificial neural networks as rainfall-rumoff models. Hydrological Science Journal, 41, 399-417.
10. Zhang, Q. and Stanley, S.J. (1997) Forecasting raw-water quality parameters for the North Saskatchewan river by neural network modelling. Water Research, 31, 2340-2350.
11. Sajikumar, N. and Thandaveswara, B.S. (1999) A non-linear rainfall-runoff model using an artificial neural network. Journal of Hydrology, 216, 32-55.
12. Zealand, C.M., Burn, D.H. and Simonovic, S.P. (1999) Short tern streamflow forecasting using artificial neural networks. Journal of Hydrology, 214, 32-48.
13. Anmala, J., Zhang, B. and Govindaraju, R.S. (2000) Comparison of ANNs and empirical approaches for predicting watershed runoff. Journal of Water Resources Planning and Management, 126, 156-166.
14. Coulibaly, P., Anctil, F. and Bobbe, B. (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology, 230, 244-257.
15. Govindaraju, R.S. and Rao, A.R. (2000) Artificial neural networks in hydrology. Kluwer Academic Publishers, Amsterdam.
16. Shamseldin, A.Y., O'Connor, K.M. and Liang, G.C. (1997) Methods for combining the outputs of different rainfall-runoff models. Journal of Hydrology, 197, 203-229.
17. Agarwal, J. and Singh, R.D. (2004) Runoff modelling through back propagation artificial neural networks with variable rainfall-runoff data. Water Resources Management, 18, 285-300.
18. Kumar, D.N, Raju, K.S. and Sathish, T. (2004) River flow forecasting using recurrent neural networks. Water Resources Management, 18, 143-161.
19. Sahin, U., Ucan, O.N., Soyhan, B. and Bayat, C. (2004) Modeling of CO distribution in Istanbul using artificial neural networks. Fresenius Environmental Bulletin, 13 (9), 839-845.
20. Freiwan, M. and Cigizoglou, H.K. (2005) Prediction of total monthly rainfall in Jordan using feed forward backpropagation method. Fresenius Environmental Bulletin, 14 (2), 142-151.
21. Lekkas, D.F., Onof, C., Lee, M.J. and Baltas, E.A. (2004) Application of artificial neural networks for flood forecasting. Global Nest: the International Journal, 6 (3), 205-211.
22. Makropoulos, C.K. and Butler, D. (2005) A neurofuzzy spatial decision support system for pipe replacement prioritization. Urban Water Journal, 2 (3), 141-150.
23. Alvisi, S., Mascellani, G., Franchini, M. and Bardossy, A. (2006) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Science, 10, 1-17.
24. Fausett, L. (1994) Fundamentals of neural networks architectures, Algorithms and applications. Prentice Hall, USA.
25. Haykin, S. (1994) Neural networks: A comprehensive foundation. Macmillan, New York, USA.
26. Dowla, U.F. and Rogers, L. (1995) Solving problems in environmental engineering and geosciences with artificial neural networks. Massachusetts, USA.
27. Patterson, D. (1996) Artificial neural networks. Prentice Hall, Singapore.
28. Gurney, K. (1999) An introduction to neural networks. UCL Press, UK.
29. Fahlman, S.E. and Lebiere, C. (1990) The cascade correlation learning architecture, in D.S. Touretsky (ed.), Advances in Neural Information Processing Systems 2, San Mateo, CA, Morgan Kaufmann, 524-532.
30. nd Ed., Wiley, New York.
31. ++. Academic Press, San Diego, CA.
32. Rivals, I. and Personnaz, L. (1998) A recursive algorithm based on the extended Kalman filter for the training of feed forward neural models. Neurocomputing, 20(1-3), 279-294.
33. nd ed. New York: Wiley.
34. Demuth, H. and Beale, M. (2001) Neural network toolbox. For use with Matlab®, User's Guide, V4.
35. Li, S., Wunsch, D.C., O'Hair, E. and Giesselmann, M.G. (2002) Extended Kalman filter training of neural networks on a SIMD Parallel Machine. Journal of Parallel and Distributed Computing, 62, 544-562.
36. Drago, G. P. and Ridella, S. (1994) Convergence properties of cascade correlation in Function Approximation. Neural Computing & Applications, 2, 142-147.
37. Geman, S., Bienenstock, E. and Doursat, R. (1992) Neural networks and the bias/variance dilemma. Neural Computing, 4, 1-58.
38. Diamantopoulou, M. (2005) Artificial neural networks as an alternative tool in pine bark volume estimation. Computer and Electronics in Agriculture, 48, 235-244.
39. Diamantopoulou, M., Antonopoulos, V. and Papamichail, D. (2007) Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers. Water Resources Management, 21, 649-662.
40. Box, G.E.P. and Jenkins, G.M. (1976) Time series analysis: Forecasting and control. Revised Edition. Holden Day, Inc., San Francisco, California.
41. Papamichail, D.M. and Papazafiriou, Z.G. (1992) Multiple input-single output linear functional models for river flow routing. Journal of Hydrology, 133, 365-377.
42. Coulibaly, P., Anctil, F. and Bobee, B. (2001) Multivariate reservoir inflow forecasting using temporal neural networks. Journal of Hydrologic Engineering, 6(5), 367-376.