Use of a supercomputer to advance parameter optimisation using genetic algorithms

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Authors
Fernando, Achela
Jayawardena, Amithirigala
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2007-10
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
genetic algorithms
tank model
parallel processing computers
parameter optimisation
rainfall-runoff process
ANZSRC Field of Research Code (2020)
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
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.
Publisher
IWA Publishing
Link to ePress publication
DOI
10.2166/hydro.2007.006
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
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