A nonlinear perturbation model based on artificial neural network

Journal of Hydrology

Volumn 333, Issue 2-4, 2007, Pages 504-516

  Pang, Bo ^a   Guo, Shenglian ^a   Xiong, Lihua ^a   Li, Chaoqun ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Artificial neural networks; Flood forecasting; Linear perturbation model; Nonlinear perturbation model

Indexed keywords

MATHEMATICAL MODELS; NEURAL NETWORKS; NONLINEAR SYSTEMS; PERTURBATION TECHNIQUES; RAIN; RUNOFF; WEATHER FORECASTING;

FLOOD FORECASTING; LINEAR PERTURBATION MODEL; NONLINEAR PERTURBATION MODEL;

HYDROLOGY;

HYDROLOGY; MATHEMATICAL MODELS; NEURAL NETWORKS; NONLINEAR SYSTEMS; PERTURBATION TECHNIQUES; RAIN; RUNOFF; WEATHER FORECASTING;

ARTIFICIAL NEURAL NETWORK; FLOOD FORECASTING; NONLINEARITY; PERTURBATION; RAINFALL-RUNOFF MODELING; SIMULATION; WATERSHED;

ASIA; CHINA; EURASIA; FAR EAST;

EID: 33845975322     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2006.09.015     Document Type: Article

References (41)

1. Campolo M., Andreussi P., and Soldati A. River flow forecasting with a neural network model. Water Resour. Res. 35 (1999) 1191-1197
2. Campolo M., Soldati A., and Andreussi P. Artificial neural network approach to flood forecasting in the River Arno. Hydrol. Sci. J. 48 (2003) 381-398
3. Chang F., and Chen Y. A counter propagation fuzzy-neural network modeling approach to real time streamflow prediction. J. Hydrol. 245 (2001) 153-164
4. Clark C.O. Storage and unit hydrograph. Trans. ASCE 110 (1945)
5. Dawson C.W., Abrahart R.J., Shamseldin A.Y., and Wilby R.L. Flood estimation at ungauged sites using artificial neural networks. J. Hydrol. 319 (2006) 391-409
6. Dekker S.C., Bouten W., and Schaap M.G. Analysing forest transpiration model errors with artificial neural networks. J. Hydrol. 246 (2001) 197-208
7. French M.N., Krajewski W.F., and Cuykendall R.R. Rainfall forecasting in space and time using a neural network. J. Hydrol. 137 (1992) 1-31
8. Friedman M., and Kandel A. Introduction to Pattern Recognition: Statistical, Structural, Neural, and Fuzzy Logic Approaches. Series in Machine Perception Artificial Intelligence 32 (1999), World Scientific, Singapore
9. Gautam M.R., Watanabe K., and Saegusa H. Runoff analysis in humid forest catchment with artificial neural network. J. Hydrol. 235 (2000) 117-136
10. Haykin S. Neural Networks (1994), MacMillan College Publishing Company, New York, USA
11. Hsu K.L., Gupta H.V., and Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 31 (1995) 2517-2530
12. Imrie C.E., Durucan S., and Korre A. River flow prediction using artificial neural networks: generalisation beyond the calibration range. J. Hydrol. 233 (2000) 138-153
13. Jain A., Sudheer K.P., and Srinivasulu S. Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol. Process 18 (2004) 571-581
14. Kachroo R.K., and Liang G.C. River flow forecasting, part 2, algebraic development of linear modelling techniques. J. Hydrol. 133 (1992) 17-40
15. Kachroo R.K., Liang G.C., and O'Connor K.M. Application of the linear perturbation model (LPM) to flood routing on the Mekong River. Hydrol. Sci. J. 33 2-4 (1988) 193-214
16. Khalil M., Panu U.S., and Lennox W.C. Groups and neural networks based streamflow data infilling procedures. J. Hydrol. 241 (2001) 153-176
17. Liang G.C., and Nash J.E. Linear models for river flow routing on large catchments. J. Hydrol. 103 (1988) 157-188
18. Luk K.C., Ball J.E., and Sharma A. Groups and neural networks based streamflow data infilling procedures. J. Hydrol. 227 (2000) 56-65
19. Minns A.W., and Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrolog. Sci. J. 41 (1996) 399-417
20. Nash J.E., and Brasi B.I. A hybrid model for flood forecasting on large catchments. J. Hydrol. 65 (1983) 125-137
21. Nash J.E., and Foley J.J. Linear models of rainfall-runoff systems. Rainfall-runoff relationship, Proceedings of the International Symposium on Rainfall-runoff Modelling (1982), Water Resources Publications, Mississippi State University, USA 51-66
22. Nash J.E., and Sutcliffe J. River flow forecasting through conceptual models. J. Hydrol. 10 (1970) 282-290
23. Natale L., and Todini E. A constrained parameter estimation technique for nonlinear model in hydrology. In: Cirani T.A., Mairone U., and Wallis J.R. (Eds). Mathematical Models for surface Water Hydrology (1977), Wiley, Chichester 109-147
24. Ojha C.S.P., and Singh V.P. ANN Modeling in Watershed Hydrology. In: Singh V.P., Frevert D.K., and Meyer S.P. (Eds). Mathematical Models of Large Watershed Hydrology[M] (2001), Water Resources Publications, Littleton, Colorado 67-88
25. Rajurka M.P., Kothyari U.C., and Chaube U.C. Modelling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 285 (2004) 96-113
26. Rumelhart D.E., Hinton G.E., and Williams R.J. Leaning internal representations by error propagation. Parallel Distributed Processing: Exploration in the Microstructure of Cognition Vol. 1 (1986), MIT Press, Cambridge, MA 318-362
27. Salas J.D., Markus M., and Tokar A.A. Streamflow forecasting based on artificial neural networks. In: Gaovindraju R.S., and Rao A.R. (Eds). Artificial Neural Networks in Hydrology [M] (2000), Kluwer Academic Publishers, Dordrecht 23-51
28. Sarle, W.S., 1995. Stopped training and other remedies for over-fitting. In: Proceedings of the Twenty-seventh Symposium on the Interface of Computing Science and Statistics. pp. 352-360.
29. Shamseldin A.Y. Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199 (1997) 272-294
30. Shamseldin A.Y., and O'Connor K.M. A nonlinear neural network technique for updating river flow forecasts. Hydrol. Earth Syst. Sci. 5 (2001) 577-597
31. Sherman L.K. Stream flow from rainfall by the unit-hydrograph method. Eng. News Rec. 108 (1932)
32. Sivakumar B., Jayawardena A.W., and Fernando T.M.K.G. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J. Hydrol. 265 (2002) 225-245
33. Smith J., and Eli R.N. Neural network models of the rainfall-runoff process. J. Water Resour. Plan. Man.-ASCE 121 (1995) 499-508
34. Todini E., and Wallis J.R. Using CLS for daily or longer period rainfall-runoff modelling. In: Cirani T.A., Maione U., and Wallis J.R. (Eds). Mathematical Models for Surface Water Hydrology (1977), Wiley, Chichester 149-179
35. Wilby R.L., Abrahart R.J., and Dawson C.W. Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48 (2003) 163-181
36. Xia J. Identification of a constrained nonlinear hydrological system described by Volterra functional series. Water Resour. Res. 27 (1991) 2415-2420
37. Xia J., O'Connor K.M., Kachroo R.K., and Liang G.C. A nonlinear perturbation model considering catchment wetness and its application in river flow forecasting. J. Hydrol. 200 (1997) 164-178
38. Xiong L., and Guo S.L. Effects of the catchment runoff coefficient on the performance of TOPMODEL in rainfall-runoff modeling. Hydrol. Process 18 (2004) 1823-1836
39. Xiong L., and O'Connor K.M. Comparison of four updating models for real-time river flow forecasting. Hydrol. Sci. J. 47 (2002) 621-639
40. Xiong L., O'Connor K.M., and Guo S.L. Comparison of three updating schemes using Artificial Neural Network in flow forecasting. Hydrol. Earth Syst. Sci. 8 2 (2004) 247-255
41. Zhu M.L., Fujita M., and Hashimoto N. Application of neural networks to runoff prediction. In: Hipel K.W., et al. (Ed). Stochastic and Statistical Methods in Hydrology and Environmental Engineering (1994), Kluwer, Dordrecht, The Netherlands 205-216