Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

Abstract

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

Systematic Assessment on Waterlogging Control Facilities in Hefei City of Anhui Province in East China

“…from of those of this study area, which is the limitation of this study. Additionally, the model parameters were based on location engineering technical data and measured value in this area because the essential facilities were not actually built in the study area at that time; thus, there was not much for the model to predict under different actual rainfall events (see Table S6), which was similar to what it was like in previous study [26]. In spite of this, the main contributions of this study are still invaluable.…”

Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

“…In the SWMM model, parameters are typically classified into two types: those that can be directly measured through field surveys, and those that lack a precise physical definition, which makes them challenging to directly obtain. The latter type is usually estimated based on empirical ranges, which require the identification of sensitive parameters that significantly impact the model to improve the efficiency of calibration [31].…”

“…In this paper, the improved Morris screening method [31][32][33] is selected for sensitivity analysis, i.e., the idea of the control variable method is adopted to change the value of a parameter in a fixed step to affect the simulation results, under the condition of ensuring that the other parameters remain unchanged, and the average rate of change of the model results is taken as the sensitivity value of the parameter, which is calculated using the following formulas:…”