Abstract
The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most widely used numerical models to simulate urban runoff and drainage. A typical SWMM project has hundreds or thousands of sub-catchments and more than 20 parameters associated with six different physical processes for each sub-catchment. Consequently, model calibration is a challenging task. In this study, SWMM was integrated with the Optimization Software Tool for Research Involving Computational Heuristics (OSTRICH) to perform single- and multi-objective automatic calibration. The newly developed OSTRICH-SWMM is an open-source tool with dozens of parallelized optimization algorithms. A catchment in Buffalo, NY was selected as a case study and was calibrated according to two competing criteria: (1) minimizing errors in simulated peak flow, and (2) minimizing errors in total flow volume. The Pareto front for the case study was obtained using a multi-objective calibration algorithm and this allowed for evaluating tradeoffs between the peak flow and total volume criteria. The results demonstrate that OSTRICH-SWMM is a promising tool for automatic calibration of SWMM models.
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