Forecasting daily streamflow using hybrid ANN models

Journal of Hydrology

Volumn 324, Issue 1-4, 2006, Pages 383-399

  Wang, Wen ^a,b   Gelder, Pieter H A J M Van ^b   Vrijling, J K ^b   Ma, Jun ^c  

^a HOHAI UNIVERSITY   (China)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^c YELLOW RIVER INSTITUTE OF HYDRAULIC RESEARCH   (China)

Author keywords

Cluster based ANN; Fuzzy c means clustering; Hybrid artificial neural networks; Periodic ANN; Streamflow forecast; Threshold ANN

Indexed keywords

MATHEMATICAL MODELS; NEURAL NETWORKS; RUNOFF; TIME SERIES ANALYSIS;

ARTIFICIAL NEURAL NETWORKS (ANN); CLUSTER-BASED ANN; FUZZY C-MEANS CLUSTERING; HYBRID ARTIFICIAL NEURAL NETWORKS; PERIODIC ANN; STREAMFLOW FORECAST; THRESHOLD ANN;

STREAM FLOW;

MATHEMATICAL MODELS; NEURAL NETWORKS; RUNOFF; STREAM FLOW; TIME SERIES ANALYSIS;

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; RUNOFF; STREAMFLOW;

EID: 33646547633     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2005.09.032     Document Type: Article

References (54)

1. Abrahart R.J., and See L. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol. Process. 14 (2000) 2157-2172
2. Amari S.-I., Murata N., Muller K.-R., Finke M., and Yang H.H. Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans. Neural Netw. 8 5 (1997) 985-996
3. Bartolini P., Salas J.D., and Obeysekera J.T.B. Multivariate periodic ARMA(1,1) processes. Water Resour. Res. 24 (1988) 1237-1246
4. Becker A., and Kundzewicz Z.W. Nonlinear floodrouting with multilinear models. Water Resour. Res. 23 (1987) 1043-1048
5. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
6. Bordignon S., and Lisi F. Nonlinear analysis and prediction of river flow time series. Environmetrics 11 (2000) 463-477
7. Braddock R.D., Kremmer M.L., and Sanzogni L. Feed-forward artificial neural network model for forecasting rainfall run-off. Environmetrics (1998) 419-432
8. Dawson C.W., and Wilby R.L. Hydrological modelling using artificial neural networks. Prog. Phys. Geography 25 1 (2001) 80-108
9. De Villars J., and Barnard E. Backpropagation neural nets with one and two hidden layers. IEEE Trans. Neural Netw. 4 1 (1993) 136-141
10. Garrick M., Cunnane C., and Nash J.E. A criterion of efficiency for rainfall-runoff models. J. Hydrol. 36 (1978) 375-381
11. Hipel K.W., and Mcleod A.I. Time Series Modelling of Water Resources and Environmental Systems (1994), Elsvier, Amsterdam
12. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Netw. 2 5 (1989) 359-366
13. Hsu K., Gupta H.V., and Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 31 (1995) 2517-2530
14. Hu T.S., Lam K.C., and Ng S.T. River flow time series prediction with a range-dependent neural network. Hydrol. Sci. J. 46 5 (2001) 729-745
15. Jacobs R.A., and Jordan M.I. Learning piecewise control strategies in a modular neural network architecture. IEEE Trans. Syst. Man Cybern. 23 2 (1993) 337-345
16. Jacobs R.A., Jordan M.I., Nowlan S.J., and Hinton G.E. Adaptive mixtures of local experts. Neural Comput. 3 (1991) 79-87
17. Jones R.H., and Brelsford W.M. Time series with periodic structure. Biometrika 54 (1967) 403-408
18. Jordan M.I., and Jacobs R.A. Hierarchical mixture of experts and the EM algorithm. Neural Comput. 6 (1994) 181-214
19. Kachroo R.K., and Natale L. Non-linear modelling of the rainfall-runoff relation. J. Hydrol. 135 (1992) 341-369
20. Kalman, B.L., Kwasny, S.C., 1992. Why Tanh: choosing a sigmoidal function. In: Proceedings of the International Joint Conference on Neural Networks, Baltimore, vol. 4, pp. 578-581.
21. Kantz H., and Schreiber T. Nonlinear Time Series Analysis (2004), Cambridge University Press, Cambridge
22. Kennel M.B., Brown R., and Abarbanel H.D. Determining embedding dimension for phase-space reconstruction using geometrical construction. Phys. Rev. A 45 (1992) 3403-3411
23. Kneale, P.E., See, L., Smith, A., 2001. Towards defining evaluation measures for neural network forecasting models. Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia, available at http://www.geocomputation.org/2001.
24. Legates D.R., Gregory J., and McCabe G.J. Evaluating the use of "Goodness of Fit" measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35 (1999) 233-241
25. Maier H.R., and Dandy G.C. The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ. Model. Softw. 13 2 (1998) 193-209
26. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15 (2000) 101-123
27. Minns A.W., and Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 41 (1996) 399-417
28. Nash J.E., and Sutcliffe J.V. River flow forecasting through conceptual models, I. A discussion of principles. J. Hydrol. 10 (1970) 282-290
29. Packard N.H., Crutchfield J.P., Farmer J.D., and Shaw R.S. Geometry from a time series. Phys. Rev. Lett. 45 9 (1980) 712-716
30. Pagano M. On periodic and multiple autoregressions. Ann. Stat. 6 (1978) 1310-1317
31. Pal N.R., Pal S., Das J., and Majumdar K. SOFM-MLP: a hybrid neural network for atmospheric temperature prediction. IEEE Trans. Geosci. Remote Sensing 41 12 (2003) 2783-2791
32. Rajurkar M.P., Kothyari U.C., and Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 285 (2004) 96-113
33. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back-propagating errors. Nature 323 (1986) 533-536
34. Salas J.D., and Abdelmohsen M.W. Initialization for generating single-site and multisite low-order periodic autoregressive and moving average processes. Water Resour. Res. 29 6 (1993) 1771-1776
35. Salas J.D., Boes D.C., and Smith R.A. Estimation of ARMA models with seasonal parameters. Water Resour. Res. 18 (1982) 1006-1010
36. See L., and Openshaw S. Applying soft computing approaches to river level forecasting. Hydrol. Sci. J. 44 5 (1999) 763-778
37. Shamseldin A.Y. Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199 (1997) 272-294
38. Sharkey A.J.C. On combining artificial neural nets. Connect. Sci. 8 3-4 (1996) 299-313
39. Silverman B.W. Density Estimation (1986), Chapman Hall, London
40. Sivakumar B., Berndtsson R., and Persson M. Monthly runoff prediction using phase space reconstruction. Hydrol. Sci. J. 46 3 (2001) 377-387
41. Solomatine D.P., and Dulal K.N. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 48 3 (2003) 399-411
42. Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics 898 (1981) 366-381
43. Todini E., and Wallis J.R. Using CLS for daily or longer period rainfall-runoff modelling. In: Ciriani T.A., Maione U., and Wallis J.R. (Eds). Mathematical Models for Surface Water Hydrology (Part 2: Flood Models) (1977), Wiley 149-168
44. Tong H. Threshold Models in Non-linear Time Series Analysis (1983), Springer-Verlag, New York
45. Tong H., and Lim K.S. Threshold autoregression, limit cycles and cyclical data. J. R. Stat. Soc. B, Methodol. 42 3 (1980) 245-292
46. Vecchia A.V. Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27 (1985) 375-384
47. Wang, W., Van Gelder, P.H.A.J.M., Vrijling, J.K., Ma, J., 2004a. Predictability of streamflow processes of the Yellow River. Proceedings of the Sixth International Conference on Hydroinformatics. World Scientific, Singapore, pp. 1261-1268.
48. Wang, W., Van Gelder, P.H.A.J.M., Vrijling, J.K., 2004b. Periodic autoregressive models applied to daily streamflow. Proceedings of the Sixth International Conference on Hydroinformatics. World Scientific, Singapore, pp. 1334-1341.
49. Wang W., Van Gelder P.H.A.J.M., and Vrijling J.K. Some issues about the generalization of neural networks for time series prediction. In: Duch W. (Ed). Artificial Neural Networks: Formal Models and Their Applications. Lecture Notes in Computer Science vol. 3697 (2005) 559-564
50. Wang, W., Van Gelder, P.H.A.J.M., Vrijling, J.K., Ma, J., in press. Testing for nonlinearity of streamflow processes at different timescales. J. Hydrol.
51. Willmott C.J., Ackleson S.G., Davis R.E., Feddema J.J., Klink K.M., Legates D.R., O'Donnell J., and Rowe C.M. Statistics for the evaluation and comparison of models. J. Geophys. Res. 90 c5 (1985) 8995-9005
52. Yakowitz S., and Karlsson M. Neareast neighbour methods for time series with application to rainfall-runoff prediction. In: MacNeil J.B., and Umphrey G.J. (Eds). Stochastic Hydrology. D (1987), Reidel, Dordrecht 149-160
53. Zealand C.M., Burn D.H., and Simonovic S.P. Short term streamflow forecasting using artificial neural networks. J. Hydrol. 214 (1999) 32-48
54. Zhang B., and Govindaraju R.S. Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour. Res. 36 3 (2000) 753-762