Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization essential reliable SWMM model simulation results, which are heterogeneously sensitive to a variety parameters, especially when involving complicated conditions. This study proposed Genetic Algorithm-based parameter method that combines Morris screening with generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, used determine parameters calibration, GLUE employed narrow down range values, and Algorithm applied further optimize by considering objective constraints. results show set calibrated obtained methods, can reduce peak error 9% simulation, then multi-objective constrained reduces parameters’ in process up 6%. During validation process, determined from combination both obtain optimal values Algorithm. shows superior applicability different rainfall intensities rain-type events. These findings imply automated calibration utilizing based on combined has enhanced performance.