Application of radial basis function neural networks to short-term streamflow forecasting

Physics and Chemistry of the Earth

Volumn 35, Issue 13-14, 2010, Pages 571-581

  Kagoda, Paulo A ^a   Ndiritu, John ^a   Ntuli, Celiwe ^b   Mwaka, Beason ^b  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^b National Working for Water Programme   (South Africa)

Author keywords

Radial basis function neural networks; SCE UA; Self organizing map; Streamflow forecasting

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CONVENTIONAL APPROACH; DATA REQUIREMENTS; HYDROGRAPHS; LOW FLOW; MODEL DATA; MODEL PERFORMANCE; MODEL REQUIREMENTS; MODELING APPROACH; NON-LINEAR RELATIONSHIPS; OBJECTIVE FUNCTIONS; OBSERVED DATA; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; SCE-UA; SOUTH AFRICA; STREAMFLOW FORECASTING; STREAMFLOW MODELING; WATER RESOURCES MANAGEMENT;

ATTITUDE CONTROL; CATCHMENTS; CONFORMAL MAPPING; FORECASTING; NEURAL NETWORKS; RUNOFF; STREAM FLOW; WATER RESOURCES;

RADIAL BASIS FUNCTION NETWORKS;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CATCHMENT; FLOW MODELING; FORECASTING METHOD; NUMERICAL MODEL; SELF ORGANIZATION; STREAMFLOW; WATER MANAGEMENT;

LIMPOPO; LUVUVHU RIVER; SOUTH AFRICA;

EID: 77957016230     PISSN: 14747065     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pce.2010.07.021     Document Type: Article

References (34)

1. Alcázar J., Palau A., Vega-Garci{dotless}́a C. A neural net model for environmental flow estimation at the Ebro River Basin, Spain. Journal of Hydrology 2008, 349(1-2):44-55.
2. Anctill F., Perrin C., Andereassian A. Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environmental Modeling and Software 2004, 19:357-368.
3. ASCE Task Committee on the application of ANNs in hydrology Committee on the application of ANNs in hydrology artificial neural networks in hydrology I: preliminary concepts. Journal of Hydrologic Engineering 2000, 5(2):115-123.
4. ASCE Task Committee on the application of ANNs in hydrology Artificial neural networks in hydrology, II: hydrologic application. Journal of Hydrologic Engineering 2000, 5(2):124-137.
5. Department of Water Affairs and Forestry, 2004. National Water Resource Strategy.
6. Duan Q., Sorooshian S., Gupta V. Effective and efficient global optimization of conceptual rainfall-runoff models. Water Resources Research 1992, 28(4):1015-1031.
7. Girosi F., Pogio T. Networks and the best approximation property. Biological Cybernetics 1990, 63:169-176.
8. Hall M.J., Minns A.W. The classification of hydrologically homogeneous regions. Hydrological Sciences Journal 1999, 44(5):693-704.
9. Kagoda, P.A., Ndiritu, J.G., 2008. Analysis of the effect of parameter uncertainty in rainfall frequency estimation. In: Proceedings of the Second IASTED Africa Conference Water Resource Management (AfricaWRM 2008), September 8-10, 2008, Gaborone, Botswana. ISBN: 978-0-88986-765-9.
10. Kalteh A.M., Hiorth P., Bemdtsson R. Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environmental Modelling and Software 2008, 23(7):835-845.
11. Kasabov, N., 1996. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA.
12. Katambara Z., Ndiritu J. A fuzzy inference system for modelling streamflow: case of Letaba River, South Africa. Physics and Chemistry of the Earth 2009, 34:688-700.
13. Kégl, B., Krzyzak, A., Niemann, H., 2000. Radial basis function networks and complexity regularization in function learning and classification. In: Proceedings of the Fifteenth International Conference on Pattern Recognition, vol. 2, pp. 81-86.
14. Keskin M.E., Terzi Ö. Artificial neural network models of daily pan evaporation. Journal of Hydrologic Engineering 2006, 11(1):65-70.
15. Kohonen, T., 1990. The self-organizing map. In: Proceedings of IEEE 78(9).
16. Kohonen T. Self-Organizing Maps 2001, Springer-Verlag, Berlin Heidelgerg, ISBN 3-540-67921-9. third ed.
17. Kokkonen T.S., Jakeman A.J. A comparison of metric and conceptual approaches in rainfall-runoff modeling and its implications. Water Resources Research 2001, 37(9):2345-2352.
18. Lynch, S.D., 2004. Development of a raster database of annual, monthly and daily rainfall for Southern Africa, Water Research Commission, Pretoria, South Africa. WRC Report 1156/01/03.
19. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling & Software 2000, 15:101-124.
20. Maré, H.G., Mwaka, B., Sinha, P., 2007. Development of Simplified Drought Operating Rules. In: Proceedings of the 13th SANCIAHS Symposium. Cape Town, 6-7th September 2007.
21. Moradkhani H., Hsu K., Gupta H., Sorooshian S. Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology 2004, 295:246-262.
22. Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., Veith T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 2007, 50(3):885-900.
23. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models: Part 1. A discussion of principles. Journal of Hydrology 1970, 10(3):282-290.
24. National Water Act (Act 36 of 1998) of South Africa.
25. Ndiritu J.G., Daniell T.M. An improved genetic algorithm for rainfall-runoff model calibration and function optimization. Mathematical and Computer Modelling 2001, 33(6-7):695-706.
26. Powell M.J.D. Radial basis function for multivariable interpolation: a review. Algorithms for Approximation 1987, Clarendon Press, Oxford. J.C. Mason, M.G. Cox (Eds.).
27. Python, 2008. http://www.python.org/.
28. Schulze, R.E., 2006. Rainfall: background. In: Schulze, R.E. (Ed.), South African Atlas of Climatology and Agrohydrology. Water Research Commission, Pretoria, RSA, WRC Report 489/1/06, Section 6.1.
29. Schulze, R.E., Dent, M.C., Lynch, S.D., Schäfer, N.W., Kienzle, S.W., Seed, A.W., 1994. Rainfall. In: Schulze, R.E. (Ed.), Hydrology and Agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modelling System. Report TT69/95. Water Research Commission, Pretoria, pp. AT3-25-AT3-26. ISBN 1 86845 136 4.
30. Tarboton, K.C., 1991. Daily rainfall estimation for distributed catchment modelling. In: Proceedings of the Fifth South African National Hydrological Symposium, 7-8 November 1991. SANCIAHS, Pretoria, South Africa.
31. Thirumalaiah K., Deo M.C. River stage forecasting using artificial neural networks. Journal of Hydrologic Engineering 1998, 3(1):26-32.
32. Thyer M., Kuczera G., Wang Q.J. Quantifying parameter uncertainty in stochastic models using the Box-Cox transformation. Journal of Hydrology 2002, 265:246-257.
33. Vrugt J., ter Braak C., Gupta H., Robinson B. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?. Stochastic Environmental Research and Risk Assessment 2009, 23(7):1011-1026.
34. Yapo P.O., Gupta H.V., Sorooshian S. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology 1996, 181:23-48.