Bayesian neural network for rainfall-runoff modeling

Water Resources Research

Volumn 42, Issue 7, 2006, Pages

  Khan, Mohammad Sajjad ^a   Coulibaly, Paulin ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; PRESSURE DISTRIBUTION; RAIN; RIVERS; RUNOFF;

BAYESIAN LEARNING; BAYESIAN NEURAL NETWORKS; OBJECTIVE FUNCTION; RIVER FLOW;

HYDROLOGY;

COMPUTER SIMULATION; HYDROLOGY; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; PRESSURE DISTRIBUTION; RAIN; RIVERS; RUNOFF;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; GAUSSIAN METHOD; OPTIMIZATION; RAINFALL-RUNOFF MODELING; RIVER FLOW; WATER RESOURCE;

CANADA; NORTH AMERICA;

HEPATITIS B VIRUS;

EID: 33748029144     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2005WR003971     Document Type: Article

References (38)

1. Arheimer, B., and M. Brandt (1998), Modelling nitrogen transport and retention in the catchments of southern Sweden, Ambio, 27, 471-480.
2. ASCE Task Committee on Application of Artificial Neural Network in Hydrology (2000a), Artificial neural network in hydrology. I: Preliminary concepts, J. Hydrol. Eng., 5, 115-123.
3. ASCE Task Committee on Application of Artificial Neural Network in Hydrology (2000b), Artificial neural network in hydrology. II: Hydrologic applications, J. Hydrol. Eng., 5, 124-137.
4. Bergström, S. (1976), Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Rep. 7, Norrkoping, Sweden.
5. Bishop, C. M. (1995), Neural Network for Pattern Recognition, Clarendon, Oxford, U. K.
6. Brandt, M. (1990), Simulation of runoff and nitrate transport from mixed basins in Sweden, Nord. Hydrol, 21, 13-34.
7. Buntine, W. L., and A. S. Weigend (1991), Bayesian back-propagation, Complex Syst, 5, 603-643.
8. Cannon, A. J., and P. H. Whitfield (2002), Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models, J. Hydrol., 259, 136-151.
9. Coulibaly, P., and C. K. Baldwin (2005), Nonstationary hydrological time series forecasting using nonlinear dynamic methods, J. Hydrol, 307, 164-174.
10. Coulibaly, P., F. Anctil, and B. Bobée (1999), Hydrological forecasting using artificial neural networks: The state of the art (in French), Can. J. Civ. Eng., 26, 293-304.
11. Coulibaly, P., F. Anctil, and B. Bobée (2000), Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 230, 244-257.
12. Coulibaly, P., F. Anctil, and B. Bobée (2001a), Multivariate reservoir inflow forecasting using temporal neural networks, J. Hydrol. Eng., 6, 367-376.
13. Coulibaly, P., B. Bobée, and F. Anctil (2001b), Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection, Hydrol. Processes, 15, 1533-1536.
14. Dawson, C. W., and R. L. Wilby (2001), Hydrological modeling using artificial neural networks, Prog. Phys. Geogr., 25, 80-108.
15. Dibike, Y., and P. Coulibaly (2005), Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models, J. Hydrol, 307(1-4), 145-163.
16. Halff, A. H., H. M. Halff, and M. Azmoodeh (1993), Predicting runoff from rainfall using neural networks, in Proceedings of Engineering Hydrology, pp. 760-765, Am. Soc. of Civ. Eng., Reston, Va.
17. Hsieh, W. W., and B. Tang (1998), Applying neural network models to prediction and data analysis in meteorology and oceanography, Bull. Am. Meteorol. Soc., 79, 1855-1870.
18. Jain, A., and S. Srinivasulu (2004), Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques, Water Resour. Res., 40, W04302, doi:10.1029/2003WR002355.
19. Lampinen, J., and A. Vehtari (2001), Bayesian approach for neural networks-review and case studies, Neural Networks, 14, 257-274.
20. Legates, D. R., and G. J. McCabe (1999), Evaluation the use of "goodness-of-fit" measures in hydrologie and hydroclimatic model validation, Water Resour. Res., 35, 233-241.
21. Lidén, R., and J. Harlin (2000), Analysis of conceptual rainfall-runoff modelling performance in different climates, J. Hydrol., 238, 231-247.
22. Lindström, G., M. Gardelin, B. Johansson, M. Persson, and S. Bergström (1996), HBV-96-En areellt fördelad modell for vattenkrafthydrologin, Rep. RH 12, Swed. Meteorol. and Hydrol. Inst., Norrköping.
23. MacKay, D. J. C. (1992), Bayesian interpolation, Neural Comput., 4, 720-736.
24. Maier, H. R., and G. C. Dandy (2000), Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications, Environ. Model. Software, 15, 101-123.
25. Møoller, M. (1993), A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 525-533.
26. Nabney, I. T. (2004), Netlab Algorithms for Pattern Recognition, Springer, New York.
27. Nash, J. E., and J. V. Sutcliffe (1970), River flow forecasting through conceptual models, Part 1: Discussion principles, J. Hydrol., 10, 282-290.
28. Neal, R. M. (1993), Bayesian learning via stochastic dynamics, in Advances in Neural Information Processing Systems 5, edited by C. L. Giles et al., pp. 475-482, Elsevier, New York.
29. Neal, R. M. (1996), Bayesian Learning for Neural Networks, Springer, New York.
30. Rumelhart, D. E., R. Durbin, R. Golden, and Y. Chauvin (1995), Back-propagation: The basic theory, in Back Propagation: Theory, Architectures, and Applications, edited by Y. Chauvin and D. E. Rumelhart, pp. 1-34, Lawrence Erlbaum, Hillsdale, N. J.
31. Sarle, W. S. (1995), Stopped training and other remedies for overfilling, paper presenled al the 27th Symposium on lhe Interface for Computing Science and Statistics, Carnegie Mellon University, Pittsburgh, Pa.
32. Shaw, E. M. (1998), Hydrology in Practice, CRC Press, Boca Ralon, Fla.
33. Shamseldin, A. Y. (1997), Applicalion of a neural network technique to rainfall-runoff modeling, J. Hydrol., 199, 272-294.
34. Swedish Meteorological and Hydrological Institute (SMHI) (1996), Integrated hydrological modeling system (IHMS) manual version 4.5, report, Norrköping, Sweden.
35. Tangang, F. T., B. Tang, A. H. Monahan, and W. W. Hsieh (1998), Forecasting ENSO events: A neural network-Extended EOF approach, J. Clim., 11, 29-41.
36. Tokar, A. S., and P. A. Johnson (1999), Rainfall-runoff modeling using artificial neural networks, J. Hydrol. Eng., 4, 232-239.
37. Walker, A. M. (1969), On the asymptotic behaviour of posterior distribu-tions, J. R. Stat. Soc., 31, 80-88.
38. Williams, P. M. (1995), Bayesian regularization and pruning using a Laplace prior, Neural Comput., 7, 117-143.