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    Peak-Flow Forecasting with Genetic Algorithm and SWMM

    Source: Journal of Hydraulic Engineering:;1995:;Volume ( 121 ):;issue: 008
    Author:
    Shie-Yui Liong
    ,
    Weng Tat Chan
    ,
    Jaya ShreeRam
    DOI: 10.1061/(ASCE)0733-9429(1995)121:8(613)
    Publisher: American Society of Civil Engineers
    Abstract: The success of a catchment model is known to depend a great deal on the catchment-model calibration scheme applied to it. This paper presents the application of a genetic algorithm (GA) in the search for the optimal values of catchment calibration parameters. GA is linked to a widely used catchment model, the storm water management model (SWMM), and applied to a catchment in Singapore of about 6.11 km
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      Peak-Flow Forecasting with Genetic Algorithm and SWMM

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    http://yetl.yabesh.ir/yetl1/handle/yetl/24181
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    contributor authorShie-Yui Liong
    contributor authorWeng Tat Chan
    contributor authorJaya ShreeRam
    date accessioned2017-05-08T20:42:23Z
    date available2017-05-08T20:42:23Z
    date copyrightAugust 1995
    date issued1995
    identifier other%28asce%290733-9429%281995%29121%3A8%28613%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/24181
    description abstractThe success of a catchment model is known to depend a great deal on the catchment-model calibration scheme applied to it. This paper presents the application of a genetic algorithm (GA) in the search for the optimal values of catchment calibration parameters. GA is linked to a widely used catchment model, the storm water management model (SWMM), and applied to a catchment in Singapore of about 6.11 km
    publisherAmerican Society of Civil Engineers
    titlePeak-Flow Forecasting with Genetic Algorithm and SWMM
    typeJournal Paper
    journal volume121
    journal issue8
    journal titleJournal of Hydraulic Engineering
    identifier doi10.1061/(ASCE)0733-9429(1995)121:8(613)
    treeJournal of Hydraulic Engineering:;1995:;Volume ( 121 ):;issue: 008
    contenttypeFulltext
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