Peak-Flow Forecasting with Genetic Algorithm and SWMM

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...

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Bibliographic Details
Published in:Journal of hydraulic engineering (New York, N.Y.) N.Y.), 1995-08, Vol.121 (8), p.613-617
Main Authors: Liong, Shie-Yui, Chan, Weng Tat, ShreeRam, Jaya
Format: Article
Language:English
Subjects:
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hydrology
Hydrology. Hydrogeology
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Summary: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 2 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%.
ISSN:0733-9429
1943-7900
DOI:10.1061/(ASCE)0733-9429(1995)121:8(613)