Parameter optimization of SWMM for runoff quantity and quality calculation in a eutrophic lake watershed using a genetic algorithm

Currently, various calibration methods including manual and automatic ways have been used to calibrate rainfall-runoff models. This study used a genetic algorithm(GA) to accurately reflect the SWMM output of each parameter set for the parameter optimization and to reduce the possibility of local optimum solution. To integrate the SWMM and GA, subroutines in the SWMM source code were modified. A developed program was applied to the Jangcheon catchment in Youngrang Lake watershed. Parameters that sensitively affected the runoff flow and water quality calculation were determined using the GA. For runoff flow calculation, eight parameters in the runoff block and one parameter in the transport block were calibrated. Four parameters in the runoff block were also calibrated for the water quality calculation. The validated SWMM then estimated the runoff pollutant loads flowing into Youngrang Lake discharged from the Jangcheon catchment.