Please use this identifier to cite or link to this item: https://doi.org/10.1061/(ASCE)0733-9429(1995)121:8(613)
Title: Peak-flow forecasting with genetic algorithm and SWMM
Authors: Liong, Shie-Yui 
Chan, Weng Tat 
ShreeRam, Jaya
Issue Date: Aug-1995
Citation: Liong, Shie-Yui, Chan, Weng Tat, ShreeRam, Jaya (1995-08). Peak-flow forecasting with genetic algorithm and SWMM. Journal of Hydraulic Engineering 121 (8) : 613-617. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)0733-9429(1995)121:8(613)
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 km2 in size. Six storms were considered: three for calibration and three for verification. The study shows that GA requires only a small number of catchment-model simulations and yet yields relatively high peak-flow prediction accuracy. The prediction error ranges from 0.045% to 7.265%.
Source Title: Journal of Hydraulic Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/84648
ISSN: 07339429
DOI: 10.1061/(ASCE)0733-9429(1995)121:8(613)
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