Storm Water Management Model Parameter Optimization in Urban Watershed Using Sewer Level Data

The growth of severe rain storm in the world has increased flood damage severely, and the precipitation distribution is getting more erratic. The unpredictability in precipitation increases the seriousness of the existing flood damage especially during rainy seasons. Structural measures such as installation or expansion of drainage facilities and improvement of reverse gradient of sewer pipes can be applied to decrease the flood damage. However, these measures require high cost, lots of time, and large site. For these reasons, non-structural measures can be alternatives, and a rainfall-runoff analysis model must be established to apply non-structural measures. SWMM (Storm Water Management Model) is a representative model for rainfall-runoff analysis of urban watersheds. While this model is based on many parameters and provides relatively reliable results, it contains many ambiguous parameters. Therefore, parameter estimation is essential and can be done using optimization algorithms. In present study, harmony search algorithm, one of the widely known meta-heuristic algorithms was used to automatically estimate the parameters of the SWMM. Unlike the previous other studies, the parameters were estimated by considering not only the inflow data but also the sewer level data. After the calibration of the model, other rainfall events were applied to confirm the validity of the model. The proposed methodology was applied to a watershed in Yongdap pump station basin, Seongdong-gu Seoul, South Korea. The parameter estimation of SWMM using both inflow data and sewer level data in urban watershed showed reasonable results compared to results of common methodology which considering only inflow data.