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Neural Network Based Decision Support Model for Optimal Reservoir Operation

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Abstract

A decision support model (DSM) has been developed using the artificial neural networks (ANN) for optimal operation of a reservoir in south India. The DSM developed is a combination of a rule based expert system and ANN models, which are trained using the results from deterministic single reservoir optimisation algorithm. The developed DSM is also flexible to use multiple linear regression equations instead of trained neural network models for different time periods. A new approach is tried with the DSM based on trained neural network models, which use real time data of previous time periods for deciding operating policies. The developed DSM based on ANN outperforms the regression based approach.

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References

  • Bhaskar, N. R. and Whitlach, E. E., Jr., 1980, ‘Derivation of monthly reservoir release policies’, Water Resour. Res. 16(6), 987–993.

    Google Scholar 

  • Cancelliere, A., Giuliano, G., Ancarani, A., and Rossi, G., 2002, ‘A neural networks approach for deriving irrigation reservoir operating rules’, Water Resour. Manage. 16(1), 71–88.

    Article  Google Scholar 

  • Chandramouli, V. and Raman, H., 2001, ‘Multi reservoir modeling with dynamic programming and neural network’, J. Water Resour. Plann. Manage. ASCE, 127(2), 89–98.

    Article  Google Scholar 

  • Elshorbagy, A., Simonovic, S. P., and Panu, U. S., 2000, ‘Performance evaluation of artificial neural networks for runoff prediction’, J. Hydrol. Engg. ASCE, 5(4), 424–427.

    Article  Google Scholar 

  • French, M. N., Krajewski, W. F., and Cuykendal, R. R., 1992, ‘Rainfall forecasting in space and time using a neural network’, J. Hydrol. 137, 1–37.

    Article  Google Scholar 

  • Hsu, K. L., Guptha, H. V., and Sorooshian, S., 1995, ‘Artificial neural network modeling of the rainfall – runoff process’, Water Resour. Res. 31(10), 2517–2530.

    Article  Google Scholar 

  • Jain, S. K., Das, A., and Srivastava, D. K., 1999, ‘Application of ANN for reservoir inlfow prediction and operation’, J. Water Resour. Plann. Manage. ASCE, 125(5), 263–271.

    Article  Google Scholar 

  • Karamouz, M. and Houck, H., 1982, ‘Annual and monthly reservoir operating rules’, Water Resour. Res. 18(5), 1337–1344.

    Google Scholar 

  • Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K., 1994, ‘Neural networks for river flow prediction’, J. Comp. Civil Engg. ASCE, 8(2), 201–220.

    Google Scholar 

  • Liong, S. Y., Lim, W. H., and Paudyal, G. N., 2000, ‘River stage forecasting in Bangladesh: Neural network approach’, J. Comp. Civil Engg. ASCE, 14(1), 1–8.

    Article  Google Scholar 

  • Loucks, D. P., Stedinger, J. R., and Haith, D. A., 1981, Water Resources Systems Planning and Analysis, Prentice-Hall, Englewood Cliffs, New Jersey.

    Google Scholar 

  • Philbrick, C. R., Jr. and Kitanidis, P. K., 1999, ‘Limitations of deterministic optimization applied to reservoir operations’, J. Water Resour. Plann. Manage. ASCE, 125(3), 135–142.

    Article  Google Scholar 

  • Raman, H. and Chandramouli, V., 1996, ‘Deriving a general operating policy for reservoirs using neural network’, J. Water Resour. Plann. Manage. ASCE, 122(5), 342–347.

    Article  Google Scholar 

  • Rogers, L. L. and Dowla, F. V., 1994, ‘Optimization of groundwater remediation using artificial neural networks with parallel solute transport modelling’, Water Resour. Res. 30(2), 457–481.

    Article  Google Scholar 

  • Rumelhart, D. E. and McClelland, J. L., 1987, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. I and II, M.I.T. Press, Cambridge, MA.

    Google Scholar 

  • Saad, M., Turgeon, A., Bigrs, P., and Duquette, R., 1994, ‘Learning disaggregation technique for the operation of long-term hydro-electric power systems’, Water Resour. Res. 30(1), 3195–3202.

    Article  Google Scholar 

  • Yeh, W., 1985, ‘Reservoir management and optimization models: A state of the art review’, Water Resour. Res. 21(12), 1797–1818.

    Google Scholar 

  • Young, G. K. Jr., 1967, ‘Finding reservoir operating rules’, J. Hydrau. Div. ASCE, 93(6), 297–321.

    Google Scholar 

  • Zhang, B. and Govindaraju, R. S., 2000, ‘Prediction of watershed runoff using Bayesian concepts and modular neural networks’, Water Resour. Res. 36(3), 753–762.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology, Guwahati, India

    V. Chandramouli & Paresh Deka

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  1. V. Chandramouli
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  2. Paresh Deka
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Correspondence to V. Chandramouli.

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Chandramouli, V., Deka, P. Neural Network Based Decision Support Model for Optimal Reservoir Operation. Water Resour Manage 19, 447–464 (2005). https://doi.org/10.1007/s11269-005-3276-2

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  • DOI: https://doi.org/10.1007/s11269-005-3276-2

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