Modelling and forecasting of hydrological variables using artificial neural networks: The Kafue River sub-basin

Hydrological Sciences Journal

Volumn 48, Issue 3, 2003, Pages 363-379

  Chibanga, Richard ^a   Berlamont, Jean ^a   Vandewalle, Joos ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

ARMAX; Artificial neural networks; Forecasting; Kafue River; Reservoir routing; Streamflows; Subsystem; Training

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; CORRELATION METHODS; FEEDFORWARD NEURAL NETWORKS; FLOW OF WATER; FORECASTING; REGRESSION ANALYSIS; RESERVOIRS (WATER); TIME SERIES ANALYSIS;

HYDROMETEORLOGICAL DATA;

HYDROLOGY;

ARTIFICIAL NEURAL NETWORK; FLOW MODELING; HYDROLOGICAL MODELING; RESERVOIR; RIVER FLOW;

KAFUE RIVER; ZAMBIA;

EID: 0037903280     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1623/hysj.48.3.363.45282     Document Type: Article

References (28)

1. Abrahart, J. R. & See, L. (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol. Processes 14, 2157-2172.
2. Atiya, F. A., El-Shoura, M. S., Shaheen, I. S. & El-Sherif, S. M. (1999) A comparison between neural network forecasting techniques-case study: river flow forecasting. IEEE Trans. Neural Networks 10(2), 402-409.
3. Bras, R. & Rodriguez-Iturbe, I. (1985) Random Functions and Hydrology, Addison Wesley, Reading, Massachusetts, USA.
4. Burke, J. & Jones, M. (1993) Kafue Basin Action Plan. Dept of Economics & Social Development (Water Resources Branch, Science, Technology, Environment, Energy & Nat. Res. Div.), United Nations, New York, USA.
5. Campolo, M., Andreussi, P. & Soldati, A. (1999) River flood forecasting with a neural network model, Water Resour. Res, 35(4), 1191-1197.
6. Chakraborty, K., Mehrotra, K., Mohan, C. K. & Ranka, S. (1992) Forecasting the behaviour of multivariate time series using neural networks. Neural Networks 5(6), 961-970.
7. Chaudhry, H. M. (1993) Open-Channel Flow. Prentice-Hall, Englewood Cliffs, New Jersey, USA. watershed models. J. Hydrol. 158, 265-284.
8. Duan, Q., Sorooshian, S. & Gupta, V. K. (1994) Optimal use of SCE-UA global optimisation method for calibrating watershed models. J. Hydrol. 158, 265-284.
9. Fernando, D. A. K. & Jayawardena, A. W. (1998) Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol., Engng 3(3). 203-209.
10. French, M. N., Krajewski, W. F. & Cuykendall, R. R. (1992) Rainfall forecasting in space and time using a neural network. J. Hydrol, 137, 1-31.
11. Funahashi, K. (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2, 183-187.
12. Gupta, V. K. (1984) The identification of conceptual watershed models. PhD dissertation, Case Western Reserve Univ., Cleveland, Ohio, USA.
13. Gupta, V. K. & Sorooshian, S. (1985) The relationship between data and precision of parameter estimates of hydrologic models, J. Hydrol. 81, 57-77.
14. Ilsu, K., Gupta, V. H. & Sorooshian, S. (1995) Artificial neural network modelling of rainfall-runoff process. Water Resour. Res. 31(10), 2517-2530.
15. Jakeman, A. J. & Hornberger, G. M. (1993) How much complexity is warranted in a rainfall-runoff model? Water Resour. Res. 29(8), 2637-2649.
16. Kang, K. W., Park, C. Y. & Kim, J. H. (1993) Neural network and its application to rainfall-runoff forecasting. Korean J. Hydrosci. 4, 1-9.
17. Kolmogorov, A. N. (1957) On the representation of continuous functions of many variables by superposition of continuous function of one variable and addition. Dokl. Akad. Nauk. SSR 114, 953-956.
18. Persson, M. & Berndtsson, R. (2001) Using neural networks for calibration of time-domain reflectometry measurements. Hydrol. Sci. J. 46(3), 389-398.
19. Pilgrim, D. H. (1976) Travel times and nonlinearity of flood runoff from tracer measurements on a small watershed. Water Resour. Res. 12(3), 487-496.
20. Rajurkar, M. P., Kothyari, U. C. & Chaube, U. C. (2002) Artificial neural networks for daily rainfall-runoff modelling. Hydrol. Sci. J. 47(6), 865-877.
21. Raman, H. & Sunilkumar, N. (1995) Multivariate modelling of water resources time series using artificial neural networks. Hydrol. Sci. J. 40(2), 145-163.
22. Ranjithan, S. & Eheart, J. W. (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour. Res. 29(3), 563-574.
23. Smith, M. (1993) Neural Networks for Statistical Modelling. Van Nostrand Reinhold, New York, USA.
24. Sorooshian, S. (1983) Surface water hydrology: online estimation. Rev. Geophys. 21(3), 706-721.
25. Tang, Z., de Almeda, C. & Fishwick, P. A. (1991) Time series forecasting using neural networks. Simulation 57(5). 303-310.
26. Tayfur, G. (2002) Artificial neural networks for sheet sediment transport, Hydrol. Sci. J. 47(6), 879-892.
27. Tokar, S. A. & Johnson, P. A. (1999) Rainfall-runoff modelling using artificial neural networks, J. Hydrol. Engng 4(3), 232-239.
28. Young, P. & Wallis, S. (1985) Recursive estimation: a unified approach to the identification, estimation, and forecasting of hydrological systems. Appl. Math. Comp. 17, 299-334.