A hybrid multi-model approach to river level forecasting;Approche multi-modèle hybride de la prévision du niveau des rivières

Hydrological Sciences Journal

Volumn 45, Issue 4, 2000, Pages 523-536

  See, L ^a   Openshaw, S ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FLOODING; MODEL; RIVER; UNITED KINGDOM; WATER FLOW;

FLOODS; FLOW OF WATER; FUZZY SETS; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; RIVERS; STATISTICAL METHODS; WATER LEVELS;

BAYESIAN METHOD;

HYDROLOGY;

FLOOD FREQUENCY; FORECASTING METHOD; MODELING; RIVER DISCHARGE;

ENGLAND; RIVER OUSE; UNITED KINGDOM;

EID: 0034254025     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626660009492354     Document Type: Article

References (17)

1. Dawson, C. W., and Wilby, R., 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrol. Sci. J, 43 (1):47–66.
2. Environment Agency. 1996. North East Region Flood Warning Manual, Part 1. Leeds, UK:Environment Agency.
3. French, M. N., Krajewski, W. F., and Cuykendall, R. R., 1992. Rainfall forecasting in space and time using a neural network. J. Hydrol, 137:1–31.
4. Hsu, K.-L., Gupta, H. V., and Sorooshian, S., 1995. Artificial neural network modeling of the rainfall-runoff process. Wat. Resour. Res, 31:2517–2530.
5. Jang, J.-S. R., Sun, C.-T., and Mizutani, E., 1997. Neuro-Fuzzy and Soft Computing. New Jersey, USA:Prentice Hall.
6. Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K., 1994. Neural networks for river flow prediction. J. Comput. Civil Engng, 8:201–220.
7. Kohonen, T., 1984. Self-Organization and Associative Memory. Berlin, Germany:Springer-Verlag.
8. Kosko, B., 1992. Neural Networks and Fuzzy Systems:A Dynamical Systems Approach to Machine Intelligence. New Jersey, USA:Prentice Hall.
9. Minns, A. W., and Hall, M. J., 1996. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J, 41 (3):399–417.
10. Moore, R. J., Jones, D. A., Black, K. B., Austin, R. M., Carrington, D. S., Tinnion, M., and Akhondi, A., 1994. “ RFFS and HYRAD:integrated systems for rainfall and river flow forecasting in real-time and their application in Yorkshire. ”. In BHS Occasional Paper 4, British Hydrological Society.
11. See, L., and Abrahart, R. J., “ Multi-model data fusion for hydrological forecasting. ”. In Comput. Geosci. (in press)
12. See, L., and Openshaw, S., 1999. Applying soft computing approaches to river level forecasting. Hydrol. Sci. J, 44 (5):763–778.
13. See, L., Abrahart, R. J., and Openshaw, S., 1998. “ An integrated neuro-fuzzy statistical approach to hydrological modelling. ”. In Proc. Third Conf. on GeoComputation Edited by:Abrahart, R. J., UK.:CD-ROM, Univ. Bristol.
14. Shamseldin, A. Y., and O'Connor, K. M., 1999. A real-time combination method for the outputs of different rainfall-runoff models. Hydrol. Sci. J, 44 (6):895–912.
15. Smith, J., and Eli, R. N., 1995. Neural-network models of rainfall-runoff process. J. Wat. Resour. Plan. Manage, 121:499–509.
16. Van der Voort, M., Dougherty, M., and Watson, S., 1996. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C—Emerging Technologies, 4:307–318.
17. Zadeh, L., 1994. Soft computing and fuzzy logic. IEEE Software, 11:48–56.