Advertisement

Water Resources Management

, Volume 21, Issue 8, pp 1393–1407 | Cite as

Application of Differential Evolution for Irrigation Planning: An Indian Case Study

  • A. VasanEmail author
  • Komaragiri Srinivasa Raju
Article

Abstract

The present paper demonstrates the applicability of population based search optimization method, namely, Differential Evolution (DE) to a case study of Mahi Bajaj Sagar Project (MBSP), India. Ten different strategies of DE are employed to assess the ability of DE for solving higher dimensional problems as an alternative methodology for irrigation planning. The parameters considered in DE are population size, crossover constant and weighting factor. Linear Programming (LP) is utilized as a comparative approach to assess the ability of DE. Comparison of results of LP and the 10 DE strategies for the given parameters indicated that both the results are comparable even for high dimensional problems. Extensive sensitivity analysis studies, performed for 3,600 combinations of above parameters for the 10 DE strategies suggested that DE/rand-to-best/1/bin strategy is the best strategy giving maximum benefits taking minimum CPU time. It is concluded that DE can be utilized for efficient planning of any irrigation system with suitable modifications.

Key words

cropping pattern differential evolution India irrigation planning linear programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdulkadri AO, Ajibefun IA (1998) Developing alternative farm plans for cropping system decision making. Agric Syst 56(4):431–442CrossRefGoogle Scholar
  2. Benli B, Kodal S (2003) A non-linear model for farm optimization with adequate and limited water supplies: application to the south-east anatolian project (GAP) region. Agric Water Manag 62:187–203CrossRefGoogle Scholar
  3. Bouwer H (2002) Integrated water management for the 21st century: problems and solutions. J Irrig Drain Eng 128(4):193–202CrossRefGoogle Scholar
  4. Deb K (1995) Optimization for engineering design: algorithms and examples. Prentice-Hall, New DelhiGoogle Scholar
  5. Doppler W, Salman AZ, Karablieh EKA, Wolff HK (2002) The impact of water price strategies on the allocation of irrigation water: the case of the Jordan valley. Agric Water Manag 55:171–182Google Scholar
  6. Frederiksen HD (1996) Water crisis in developing world: misconceptions about solutions. J Water Resour Plan Manage, ASCE 122(2):79–87CrossRefGoogle Scholar
  7. Kuo SF, Merkley GP, Liu CW (2000) Decision support for irrigation project planning using a genetic algorithm. Agric Water Manag 45:243–266CrossRefGoogle Scholar
  8. Loucks DP, Stedinger JR, Haith DA (1981) Water resources systems planning and analysis. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  9. Mahi Bajaj Sagar Project Report (1978) Government of Rajasthan, Banswara, Rajasthan, IndiaGoogle Scholar
  10. MBSP Report on Status June 2002 at a Glance (2002) Government of Rajasthan, Banswara, Rajasthan, IndiaGoogle Scholar
  11. Nagesh Kumar D, Srinivasa Raju K, Ashok B (2006) Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. J Irrig Drain Eng, ASCE 132(2):123–129CrossRefGoogle Scholar
  12. Patra KC (2002) Hydrology and water resources engineering. Narosa, New DelhiGoogle Scholar
  13. Paudyal GN, Gupta AD (1990) Irrigation planning by multilevel optimization. J Irrig Drain Eng, ASCE 116(2):273–291Google Scholar
  14. Pike JG (1995) Some aspects of irrigation system management in India. Agric Water Manag 27:95–104CrossRefGoogle Scholar
  15. Price K, Storn R (1997) Differential evolution – a simple evolution strategy for fast optimization. Dr Dobb’s J 22:18–24, 78Google Scholar
  16. Price K, Storn R (2005) Home page of differential evolution, URL:http://www.icsi.Berkeley.edu/~storn/code.html
  17. Raju KS, Nagesh Kumar D (2004) Irrigation planning using genetic algorithms. Water Resour Manag 18(2):163–176CrossRefGoogle Scholar
  18. Raju KS, Nagesh Kumar D (2006) Ranking irrigation planning alternatives using data envelopment analysis. Water Resour Manag 20(4):553–566CrossRefGoogle Scholar
  19. Ranjithan SR (2005) Role of evolutionary computation in environmental and water resources systems analysis. J Water Resour Plan Manage, ASCE 131(1):1–2CrossRefGoogle Scholar
  20. Rao SS (2003) Engineering optimization: theory and practice. New Age International (P) Limited, New DelhiGoogle Scholar
  21. Singh DK, Jaiswal CS, Reddy KS, Singh RM, Bhandarkar DM (2001) Optimal cropping pattern in a canal command area. Agric Water Manag 50:1–8CrossRefGoogle Scholar
  22. Vasan A (2005) Studies on advanced modeling techniques for optimal reservoir operation and performance evaluation of an irrigation system. PhD thesis, Birla Institute of Technology and Science, Pilani, IndiaGoogle Scholar
  23. Vasan A, Srinivasa Raju K (2004) Comparison of differential evolution and simulated annealing for reservoir system optimization: a case study in Rajasthan. National symposium on hydrology with focal theme on water quality, Roorkee, India, 51–58Google Scholar
  24. Water Resources Planning for Mahi River Basin (2001) Government of Rajasthan. Investigation, Design & Research (Irrigation) Unit, JaipurGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Civil EngineeringBirla Institute of Technology and SciencePilani, RajasthanIndia

Personalised recommendations