Calibration and validation of neural networks to ensure physically plausible hydrological modeling

Journal of Hydrology

Volumn 314, Issue 1-4, 2005, Pages 158-176

  Kingston, Greer B ^a   Maier, Holger R ^a   Lambert, Martin F ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Artificial neural networks; Calibration; Genetic algorithms; Streamflow modeling

Indexed keywords

CALIBRATION; GENETIC ALGORITHMS; HYDRAULIC MODELS; HYDROLOGY; SENSITIVITY ANALYSIS;

HYDROLOGICAL MODELING; STREAMFLOW MODELING;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CALIBRATION; GENETIC ALGORITHM; HYDROLOGICAL MODELING; MODEL VALIDATION; STREAMFLOW;

EID: 28444444200     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2005.03.013     Document Type: Article

References (30)

1. H.C. Aguiar, and R.M. Filho Neural network and hybrid model: a discussion about different modeling techniques to predict pulping degree with industrial data Chemical Engineering Science 56 2 2001 565 570
2. H. Akaike A new look at the statistical model identification IEEE Transactions of Automatic Control 19 1974 716 723
3. S.-I. Amari, N. Murata, K.-R. Müller, M. Finke, and H.H. Yang Asymptotic statistical theory of overtraining and cross-validation IEEE Transactions on Neural Networks 8 5 1997 985 996
4. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. I: preliminary concepts Journal of Hydrologic Engineering, ASCE 5 2 2000 115 123
5. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press Oxford
6. C.W. Dawson, and R.L. Wilby Hydrological modelling using artificial neural networks Progress in Physical Geography 25 1 2001 80 108
7. Q. Duan, S. Sorooshian, and V. Gupta Effective and efficient global optimization for conceptual rainfall-runoff models Water Resources Research 28 4 1992 1015 1031
8. L.V. Fausett Fundamentals of neural networks: architectures, algorithms, and applications 1994 Prentice-Hall Englewood Cliffs, NJ
9. M. Franchini, G. Galeati, and S. Berra Global optimization techniques for the calibration of conceptual rainfall-runoff models Hydrological Sciences Journal 43 3 1998 443 458
10. V.L. Freedman, V.L. Lopes, and M. Hernandez Parameter identifiability for catchment-scale erosion modelling: a comparison of optimization algorithms Journal of Hydrology 207 1-2 1998 83 97
11. D.E. Goldberg Genetic algorithms in search, optimization and machine learning 1989 Addison-Wesley Pub. Co. Reading, MA
12. G. Kuczera Efficient subspace probabilistic parameter optimization for catchment models Water Resources Research 33 1 1997 177 185
13. D.S. Lee, C.O. Jeon, J.M. Park, and K.S. Chang Hybrid neural network modeling of a full-scale industrial wastewater treatment process Biotechnology and Bioengineering 78 6 2002 670 682
14. H.R. Maier, and G.C. Dandy Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting Water Resources Research 35 8 1999 2591 2598
15. H.R. Maier, and G.C. Dandy Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environmental Modelling and Software 15 1 2000 101 124
16. Metcalf, A., Heneker, T., Lambert, M.F., Kuczera, G., Atan, I., 2002. A comparison of models for catchment runoff. In: Proceedings of the Hydrology and Water Resources Symposium (CD-ROM). Melbourne, Australia.
17. J.A. Nelder, R. Mead, and A. 1965 A simplex method for function minimization Computer Journal 7 4 1965 308 313
18. J.D. Olden, and D.A. Jackson Illuminating the 'black box': a randomization approach for understanding variable contributions in artificial neural networks Ecological Modelling 154 1-2 2002 135 150
19. J.D. Olden, M.K. Joy, and R.G. Death An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data Ecological Modelling 178 3-4 2004 389 397
20. D.E. Rumelhart, G.E. Hinton, and R.J. Williams D.E. Rumelhart J.L. McClelland Learning internal representations by error backpropagation Parallel Distributed Processing 1986 MIT Press Cambridge
21. Sarle, W.S., 2000. How to measure the importance of inputs? SAS Institute Inc., Cary, NC. URL: ftp://ftp.sas.com/pub/neural/importance.html.
22. R.S. Sexton, and R.E. Dorsey Reliable classification using neural networks: a genetic algorithm and backpropagation comparison Decision Support Systems 30 1 2000 11 22
23. R.S. Sexton, and J.N.D. Gupta Comparative evaluation of genetic algorithm and backpropagation for training neural networks Information Sciences 129 1-4 2000 45 59
24. K. Thirumalaiah, and M.C. Deo River stage forecasting using artificial neural networks Journal of Hydrologic Engineering 3 1 1998 26 32
25. M. Thyer, G. Kuczera, and B.C. Bates Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms Water Resources Research 35 3 1999 767 773
26. J.P. Vítkovský, A.R. Simpson, and M.F. Lambert Leak detection and calibration using transients and genetic algorithms Journal of Water Resources Planning and Management 126 4 2000 262 265
27. R.L. Wilby, R.J. Abrahart, and C.W. Dawson Detection of conceptual model rainfall-runoff processes inside an artificial neural network Hydrological Sciences Journal 48 2 2003 163 181
28. X. Yao Evolving artificial neural networks Proceedings of the IEEE 87 9 1999 1423 1447
29. L. Yitian, and R.R. Gu Modeling flow and sediment transport in a river system using an artificial neural network Environmental Management 31 1 2003 122 134
30. G. Zhang, B.E. Patuwo, and M.Y. Hu Forecasting with artificial neural networks: the state of the art International Journal of Forecasting 14 1 1998 35 62