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Irrigation Planning using Genetic Algorithms

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  • K. Srinivasa Raju
  • D. Nagesh Kumar

Abstract

The present study deals with the application of Genetic Algorithms(GA) for irrigation planning. The GA technique is used to evolve efficient cropping pattern for maximizing benefits for an irrigation project in India. Constraints include continuity equation, land and water requirements, crop diversification and restrictions on storage. Penalty function approach is used to convert constrained problem into an unconstrained one. For fixing GA parameters the model is run for various values of population, generations, cross over and mutation probabilities. It is found that the appropriate parameters for number of generations, population size, crossover probability, and mutation probability are 200, 50, 0.6 and 0.01 respectively for the present study. Results obtained by GA are compared with Linear Programming solution and found to be reasonably close. GA is found to be an effective optimization tool for irrigation planning and the results obtained can be utilized for efficient planning of any irrigation system. Copyright Kluwer Academic Publishers 2004

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  • K. Srinivasa Raju & D. Nagesh Kumar, 2004. "Irrigation Planning using Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 163-176, April.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:2:p:163-176
    DOI: 10.1023/B:WARM.0000024738.72486.b2
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    References listed on IDEAS

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    1. Laxmi Sethi & D. Kumar & Sudhindra Panda & Bimal Mal, 2002. "Optimal Crop Planning and Conjunctive Use of Water Resources in a Coastal River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(2), pages 145-169, April.
    2. Kuo, Sheng-Feng & Merkley, Gary P. & Liu, Chen-Wuing, 2000. "Decision support for irrigation project planning using a genetic algorithm," Agricultural Water Management, Elsevier, vol. 45(3), pages 243-266, August.
    3. Fi-John Chang & Li Chen, 1998. "Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 12(3), pages 185-198, June.
    4. Aihua Tang & Larry Mays, 1998. "Genetic Algorithms for Optimal Operation of Soil Aquifer Treatment Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 12(5), pages 375-396, October.
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    3. Ajay Singh, 2014. "Irrigation Planning and Management Through Optimization Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 1-14, January.
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    7. Noor Khan & Mukand Babel & Tawatchai Tingsanchali & Roberto Clemente & Huynh Luong, 2012. "Reservoir Optimization-Simulation with a Sediment Evacuation Model to Minimize Irrigation Deficits," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3173-3193, September.
    8. Singh, Ajay, 2014. "Simulation–optimization modeling for conjunctive water use management," Agricultural Water Management, Elsevier, vol. 141(C), pages 23-29.
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    11. A. Vasan & Komaragiri Raju, 2007. "Application of Differential Evolution for Irrigation Planning: An Indian Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(8), pages 1393-1407, August.
    12. K. Ramakrishnan & C. Suribabu & T. Neelakantan, 2010. "Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3835-3851, November.
    13. Qin Tu & Hong Li & Xinkun Wang & Chao Chen, 2015. "Ant Colony Optimization for the Design of Small-Scale Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2323-2339, May.
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    16. Reeling, Carson J. & Lee, John & Mitchell, Peter & Halimi, Ghulam Hazrat & Carver, Andrew, 2012. "Policy options to enhance agricultural irrigation in Afghanistan: A canal systems approach," Agricultural Systems, Elsevier, vol. 109(C), pages 90-100.
    17. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    18. Haddad, Malik & Sanders, David, 2018. "Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty," Operations Research Perspectives, Elsevier, vol. 5(C), pages 357-370.
    19. V. Jothiprakash & Ganesan Shanthi & R. Arunkumar, 2011. "Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2405-2423, August.
    20. Morimoto, T. & Ouchi, Y. & Shimizu, M. & Baloch, M.S., 2007. "Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms," Agricultural Water Management, Elsevier, vol. 93(1-2), pages 1-10, October.

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