A new tool for automatic calibration of the Storm Water Management Model (SWMM)

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.