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    Use of a supercomputer to advance parameter optimisation using genetic algorithms

    Fernando, Achela; Jayawardena, Amithirigala

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    Fernando - Supercomputer.pdf (108.8Kb)
    Date
    2007-10
    Citation:
    Fernando, A.K., & Jayawardena, A.W. (2007). Use of a supercomputer to advance parameter optimisation using genetic algorithms. Journal of Hydroinformatics, 9(4), 319-329. doi:10.2166/hydro.2007.006
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/1884
    Abstract
    Parameter optimisation is a significant but time consuming process that is inherent to conceptual hydrological models representing rainfall-runoff process. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified Genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 Computer is used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 390 Computer and IBM 9076 SP2 supercomputer reveals that the latter can be up to 8 times faster. The effectiveness of the modified GA is tested with two Tank Models for a hypothetical catchment and a real catchment. The former showed that the parallel GA reaches a lower overall error in reduced time. The overall RMSE expressed as a percentage of actual mean flow rate improves from a 31.8% in a serial processing computer to 29.5% on the SP2 super computer. The case of the real catchment – Shek-Pi-Tau Catchment in Hong Kong – reveals that the supercomputer enhances the swiftness of the GA and achieves objective within a couple of hours.
    Keywords:
    genetic algorithm, tank model, parallel processing computers, parameter optimisation, rainfall-runoff process
    ANZSRC Field of Research:
    091501 Computational Fluid Dynamics
    Copyright Holder:
    IWA Publishing
    Copyright Notice:
    ©IWA Publishing 2007. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics 9(4), 319-329, 2007, doi:10.2166/hydro.2007.006, and is available at www.iwapublishing.com
    Available Online at:
    http://www.iwaponline.com/jh/009/0319/0090319.pdf
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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