Application of ANN for Reservoir Inflow Prediction and Operation
Publication: Journal of Water Resources Planning and Management
Volume 125, Issue 5
Abstract
Artificial neural networks (ANNs) are new computing architectures in the area of artificial intelligence. The present study aims at the application of ANNs for reservoir inflow prediction and operation. The Upper Indravati multipurpose project, in the state of Orissa, India, has been selected as the focus area. The project has primarily two objectives: To provide irrigation to 128,000,000 ha of agricultural land and to generate 600 MW of electric power. An autoregressive integrated moving average time-series model and an ANN-based model were fitted to the monthly inflow data series and their performances were compared. The ANN was found to model the high flows better, whereas low flows were better predicted through the autoregressive integrated moving average model. Reservoir operation policies were formulated through dynamic programming. The optimal release was related with storage, inflow, and demand through linear and nonlinear regression and the ANN. The results of intercomparison indicate that the ANN is a powerful tool for input-output mapping and can be effectively used for reservoir inflow forecasting and operation.
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References
1.
Bhaskar, A. N., and Whitlatch, E. E. (1987). “Comparison of reservoir linear operation rules using linear and dynamic programming.” Water Resour. Bull., 23(6), 1027–1036.
2.
Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis, forecasting and control. Holden-Day, Oakland, Calif.
3.
Carriere, P., Mohaghegh, S., and Gaskari, R. (1996). “Performance of a virtual runoff hydrograph system.”J. Water Resour. Plng. and Mgmt., ASCE, 121(6), 421–427.
4.
Datta, B., and Burges, S. J. (1984). “Short-term, single, multiple-purpose reservoir operation: Importance of loss functions and forecast errors.” Water Resour. Res., 20(9), 1167–1176.
5.
Dawson, C. W., and Wilby, R. (1998). “An artificial neural network approach to rainfall-runoff modelling.” Hydrological Sci. J., 43(1), 47–66.
6.
French, M. N., Krajewski, W. F., and Cuykendall, R. R. (1992). “Rainfall forecasting in space and time using a neural network.” J. Hydro., Amsterdam, 137(1–4), 1–31.
7.
Heidari, M., Chow, V. T., Kokotovic, P. V., and Meredith, D. D. (1971). “Discrete differential dynamic programming approach to water resources system optimization.” Water Resour. Res., 7(2), 273–282.
8.
Hsu, K.-L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-funoff process.” Water Resour. Res., 31(10), 2517–2530.
9.
Karamouz, M., and Houck, M. H. (1987). “Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation.” Water Resour. Bull., 23(1), 1–9.
10.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.”J. Comp. in Civ. Engrg., ASCE, 8(2), 210–220.
11.
Maier, H. R., and Dandy, G. C. (1997). “Determining inputs for neural network models of multivariate time series.” Microcomputers in Civ. Engrg., 12, 353–368.
12.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall runoff models.” Hydrological Sci. J., 41(3), 399–418.
13.
Mohanty, B. R. ( 1994). “Optimization study of Upper Indravati project, Orissa,” ME thesis, Dept. of Hydro., University of Roorkee, India.
14.
Raman, H., and Sunilkumar, N. (1995). “Multivariate modelling of water resources time-series using artificial neural networks.” Hydrological Sci. J., 40(2), 145–163.
15.
Raman, H., and Chandramauli, V. (1996). “Deriving a general operating policy for reservoir using neural network.”J. Water Resour. Plng. and Mgmt., ASCE, 122(5), 342–347.
16.
Rumelhart, D. E., McLelland, J. L., and the PDP Research Group. (1986). Parallel distributed processing, explorations in the micro structure of cognition, vol. I: Foundations. MIT Press, Cambridge, Mass.
17.
Saad, M., Turgeon, A., Bigras, P., and Duquette, R. (1994). “Learning disaggregation technique for the operation of long-term hydro-electric power systems.” Water Resour. Res., 30(11), 3195–3202.
18.
Salas, J. D., Deulleur J. W., Yevjevich, V., and Lane, W. L. (1980). Applied modelling of hydrologic time series. Water Resources Publications, Littleton, Colo.
19.
Smith, J., and Eli, R. N. (1995). “Neural network models of rainfall-runoff process.”J. Water Resour. Plng. and Mgmt., ASCE, 121(6), 499–508.
20.
Weeks, W. D., and Boughton, W. C. (1987). “Tests of ARMA model forms for rainfall-runoff modelling,” J. Hydro., Amsterdam, 91, 29–47.
21.
Wood, E. F., and Szollosi-Nagy, A. (1978). “An adaptive algorithm for analyzing short-term structural and parameter changes in hydrologic prediction models.” Water Resour. Res., 14(4), 577–581.
22.
Young, G. K. (1967). “Finding reservoir operating rules.”J. Hydr. Div., ASCE, 93(6), 297–321.
Information & Authors
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Published In
Journal of Water Resources Planning and Management
Volume 125 • Issue 5 • September 1999
Pages: 263 - 271
History
Received: Jul 17, 1998
Published online: Sep 1, 1999
Published in print: Sep 1999
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