New optimization strategies for SWMM modeling of stormwater quality applications in urban area

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters. For this purpose, Mat-SWMM was modified to equip it with the capacity to provide comprehensive water quality analysis outcomes. Additionally, this research also conducts a comparative examination of two distinct types of objective functions in the optimization. Rather than depending on previous literature, this study undertook a numerical campaign to ascertain an appropriate range for the relevant parameters within the case study, thereby ensuring the optimization algorithm's efficient functionality. This research also implements an integrated event calibration approach, i.e., a novel method that calibrates all rainfall events collectively, thus improving systemic interaction representation and model robustness. The findings indicate that employing this methodology significantly enhances the reliability of the outcomes, thereby establishing a more robust procedure. The first objective function (TSS instantaneous less squared difference function, OF 1), which is widely employed in the literature, was designed to minimize the difference between observed and predicted instantaneous Total Suspended Solids (TSS) concentrations. In contrast, the second function (mass and mass peak consistency function, OF 2) considers integral model outputs, i.e., the overall mass balance, the time of the peak mass flow rate, and its intensity. The analysis of the outputs revealed that both objective functions demonstrated sufficient performance. OF 1 provided slightly better performance in predicting the TSS concentrations, whereas OF 2 demonstrated superior ability in capturing global event characteristics. Notably, the optimal parameter set identified through OF 2 aligned with the physically plausible ranges traditionally recommended in technical manuals for urban catchments. In contrast, OF 1's optimal set necessitated an expansion in the acceptable parameter ranges. Finally, from a computational burden viewpoint, OF 1 demanded a significantly higher number of function evaluations, thus implying an escalating computational cost as the range expands. Conversely, OF 2 necessitated fewer evaluations to converge toward the optimal solution.