Nonstationary hydrological time series forecasting using nonlinear dynamic methods

Journal of Hydrology

Volumn 307, Issue 1-4, 2005, Pages 164-174

  Coulibaly, Paulin ^a   Baldwin, Connely K ^b  

^a MCMASTER UNIVERSITY   (Canada)

^b UTAH STATE UNIVERSITY   (United States)

Author keywords

Hydrologic time series; Modeling; Multivariate adaptive regression splines; Nonstationarity; Recurrent neural networks

Indexed keywords

CLIMATOLOGY; COMPUTER SIMULATION; HYDROLOGY; LAKES; MATHEMATICAL MODELS; RECURRENT NEURAL NETWORKS; REGRESSION ANALYSIS; RIVERS; TIME SERIES ANALYSIS; WATER RESOURCES;

CLIMATE VARIABILITY; HYDROLOGICAL SYSTEMS; MARS MODELS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS);

WEATHER FORECASTING;

CLIMATE VARIATION; FORECASTING METHOD; HYDROLOGICAL MODELING; TIME SERIES;

CHRISTIA;

EID: 19744362941     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2004.10.008     Document Type: Article

References (36)

1. American Society of Civil Engineers (ASCE) Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology, II, hydrologic applications J. Hydrol. Eng. ASCE 5 2 2000 124 137
2. F. Anctil, and P. Coulibaly Wavelet analysis of the interannual variability in Southern Quebec streamflow J. Climate 1 2004 163 173
3. J. Bernier Statistical detection of changes in geophysical series L. Duckstein E. Parent Engineering Risk in Natural Resources Management 1994 Kluwer Dordrecht, The Netherlands 159 176
4. A. Blanco, M. Delgado, and M.C. Pegalajar A real-coded genetic algorithm for training recurrent neural networks Neural Netw. 14 2001 93 105
5. G.J. Bowden, H.R. Maier, and G.C. Dandy Optimal division of data for neural network models in water resources applications Water Resour. Res. 38 2 2002 2 11
6. G.E.P. Box, and G.M. Jenkins Time Series Analysis: Forecasting and Control Holden-Day, San Francisco, CA, USA 1976
7. P. Coulibaly, F. Anctil, and B. Bobée Hydrological forecasting using artificial neural networks: The state of the art Can. J. Civ. Eng. 26 3 1999 293 304
8. P. Coulibaly, F. Anctil, P.F. Rasmussen, and B. Bobée A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff Hydrol. Process. 14 15 2000 2755 2777
9. P. Coulibaly, F. Anctil, and B. Bobée Daily reservoir inflow forecasting using artificial neural networks with stopped training approach J. Hydrol. 230 2000 244 257
10. P. Coulibaly, F. Anctil, R. Aravena, and B. Bobée Artificial neural network modeling of water table depth fluctuations Water Resour. Res. 37 4 2001 885 897
11. I. Daubechies The wavelet transform time-frequency localization and signal analysis IEEE Trans. Inform. Theory 36 1990 961 1004
12. J.L. Elman Finding structure in time Cogn. Sci. 14 1990 179 211
13. E.A.B. Eltahir El-Niño and the natural variability in the flow of the Nile River Water Resour. Res. 32 1 1996 131 137
14. E. Foufoula-Georgiou, and P. Kumar Wavelets in Geophysics 1995 Academic Press New York
15. J.H. Friedman Multivariate adaptive regression splines Ann. Stat. 19 1991 1 141
16. C.L. Giles, S. Lawrence, and A.C. Tsoi Rule inference for financial prediction using recurrent neural networks Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering 1997 IEEE Press Piscataway, NJ pp. 253-259
17. M.T. Hagan, and M.B. Menhaj Training feedforward networks with Marquardt algorithm IEEE Trans. Neural Netw. 5 6 1994 989 993
18. S. Haykin Neural Networks: A Comprehensive Foundation 1999 Prentice-Hall Upper Saddle River, NJ
19. S. Haykin, and L. Li Nonlinear adaptive prediction of nonstationary signals IEEE Trans. Signal Process. 43 2 1995 526 535
20. M. Iatrou, T.W. Berger, and V.Z. Marmarelis Modeling of nonlinear nonstationary dynamic systems with a novel class of artificial neural networks IEEE Trans. Neural Netw. 10 2 1999 327 339
21. C.A.L. Jones, and D.J.C. MacKay A recurrent neural network for modeling dynamical systems Comput. Neural Syst. 9 1998 531 547
22. Z. Kohavi Switching and Finite Automata Theory 1978 McGraw-Hill New York
23. D. Labat, R. Ababou, and A. Mangin Rainfall-runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses J. Hydrol. 238 2000 149 178
24. U. Lall, and M. Mann The Great Salt Lake: a barometer of low-frequency climatic variability Water Resour. Res. 31 10 1995 2503 2515
25. U. Lall, T. Sangoyomi, and H.D.I. Abarbanel Nonlinear dynamics of the Great Salt Lake: Nonparametric short-term forecasting Water Resour. Res 32 4 1996 975 985
26. H.R. Maier, and G.C. Dandy Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environ. Model. Software 15 2000 101 124
27. E. Mann, U. Lall, and B. Saltzman Decadal-to-centenial-scale climate variability: insights into the rise and fall of the Great Salt Lake Geophys. Res. Lett. 22 8 1995 937 940
28. K. Narendra, and K. Parthasarathy Identification and control of dynamical systems using neural networks IEEE Trans. Neural Netw. 1 1990 4 27
29. J.E. Nash, and J.V. Sutcliffe River flow forecasting through conceptual models. Part I: a discussion of principles J. Hydrol. 10 1970 282 290
30. B.A. Pearlmutter Gradient calculations for dynamic recurrent neural networks: a survey IEEE Trans. Neural Netw. 6 5 1995 1212 1228
31. E.W. Saad, D.V. Prokhorov, and D.C. Wunsch Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks IEEE Trans. Neural Netw. 9 6 1998 1456 1470
32. Sangoyomi, T.B., 1993. Climatic Variability and Dynamics of Great Salt Lake Hydrology, PhD dissertation, Utah State University, Logan, UT, pp. 247.
33. J. Sum, C. Leung, G.H. Young, and W. Kan On the Kalman filtering method in neural network training and pruning IEEE Trans. Neural Netw. 10 1 1999 161 166
34. C. Torrence, and G.P. Campo A practical guide to wavelet analysis Bull. Am. Meteor. Soc. 79 1998 61 78
35. R.J. Williams, and J. Peng An efficient gradient-based algorithm for on-line training of recurrent network trajectories Neural Comput. 2 1990 490 501
36. P.C. Young Nonstationary time series analysis and forecasting Progress Environ. Sci. 1 1999 3 48