Skip to main content

Advertisement

SpringerLink
Log in

Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmed, J. A. and Sarma, A. K., 2002, ‘Stochastic models for synthetic streamflow generation’, Proc. Int. Conf. Recent trends Prob. Stat.: Theory Appl., Guwahati, India.

    Google Scholar 

  • Brahmaputra Board, Government of India, 1997, ‘Detail Project Report for Pagladia Multipurpose Reservoir’, Government of India.

  • Burn, D. H. and Yulianti, J. S., 2001, ‘Waste-load allocation using genetic algorithms’, J. Water Resour. Plan. Manage. ASCE 127(2), 121–129.

    Article  Google Scholar 

  • Cieniawski, S. E., Eheart, J. W., and Ranjithan, S., 1995, ‘Using genetic algorithms to solve a multiobjective groundwater monitoring problem’, Water Resour. Res. 31(2), 399–409.

    CAS  Google Scholar 

  • Deb, K., 2000, ‘An efficient constraint handling method for genetic algorithms’, Comput. Methods Appl. Mech. Eng. 186, 311–338.

    Google Scholar 

  • Esat, V. and Hall, M. J., 1994, ‘Water resources system optimization using genetic algorithms’, Hydroinformatics ‘94, Proceedings of the First International Conference on Hydroinformatics, Balkema, Rotterdam, The Netherlands, pp. 225–231.

    Google Scholar 

  • Forrest, S., 1993, ‘Genetic algorithms: Principles of natural selection applied to computation’, Science 261(13), 872–878.

    CAS  PubMed  Google Scholar 

  • Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Goldberg, D. E. and Deb, K., 1990, ‘A comparative analysis of selection schemes used in genetic algorithms’, in Foundation of Genetic Algorithms, Morgan Kaufman, San Mateo, CA, pp. 69–93.

    Google Scholar 

  • Goldberg, D. E. and Kuo, C. H., 1987, ‘Genetic algorithms in pipeline optimization’, J. Comp. Civil Eng. ASCE 1(2), 128–141.

    Google Scholar 

  • Hilton, A. B. C. and Culver, T. B., 2000, ‘Constraint handling for genetic algorithms in optimal remediation design’, J. Water Resour. Plann. Manage. ASCE 126(3), 128–137.

    Google Scholar 

  • Holland, J. H., 1992, Adaption in Natural and Artificial Systems, 2nd edn., Massachusetts Institute of Technology, Cambridge.

    Google Scholar 

  • Karamouz, M. and Houck, H., 1987, ‘Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation’, Water Resour. Bull. 23(1), 1–9.

    Google Scholar 

  • Loucks, D. P., Stedinger, J. R., and Haith, D. A., 1981, Water Resources System Planning and Analysis, Prentice Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • McKinney, D. C. and Lin, M. D., 1994, ‘Genetic algorithm solution of groundwater management models’, Water Resour. Res. 30(6), 1987–1906.

    Google Scholar 

  • Oliveira, R. and Loucks, D. P., 1997, ‘Operating rules for multireservoir systems’, Water Resour. Res. 33(4), 839–852.

    Google Scholar 

  • Sarma, A. K. and Ahmed, J. A., 2003, ‘Neural network-based model for synthetic streamflow generation’, J. Hydrol. Eng, ASCE.

  • Tejada-Guibert, J. A., Johnson S. A., and Stedinger, J. R., 1993, ‘Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming’, Water Resour. Res. 29(12), 3969–3980.

    Google Scholar 

  • Wang, Q. J., 1991, ‘The genetic algorithm and its application to calibrating conceptual rainfall-runoff models’, Water Resour. Res. 27(9), 2467–2471.

    Google Scholar 

  • Wardlaw, R. and Sharif, M., 1999, ‘Evaluation of genetic algorithms for optimal reservoir system operation’, J. Water Resour. Plann. Manage. ASCE 125(1), 25–33.

    Google Scholar 

  • Yakowitz, S., 1982, ‘Dynamic programming review’, Water Resour. Res. 18(4), 673–696.

    Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology, Guwahati, Guwahati, 781039, India

    Juran Ali Ahmed & Arup Kumar Sarma

Authors
  1. Juran Ali Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arup Kumar Sarma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juran Ali Ahmed.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ahmed, J.A., Sarma, A.K. Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir. Water Resour Manage 19, 145–161 (2005). https://doi.org/10.1007/s11269-005-2704-7

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-005-2704-7

Key words

Search

Navigation