Short-term Prediction of Urban Inundation based on Radar-predicted Rainfall through Real-time SWMM Parameter Calibration

Abstract

In recent times, the rapid pace of climate change has been causing urban inundation in areas where drainage of inland water is difficult. In response to this concern, studies that use SWMM are being actively conducted to predict urban inundation. However, the parameters applied to SWMM use historical values, resulting in suboptimal accuracy in predictions. Therefore, this study employed the genetic algorithm, among various optimization techniques, to bolster the accuracy of rainfall-runoff analysis of the SWMM model. This was achieved by linking ASOS data and real-time ground-observation rainfall to real-time water gauge data. Consequently, the water level could now be predicted by applying radar predicted rainfall at 10-minute intervals to the completed SWMM module. The required time for prediction of urban inundation was about 2 minutes. This timeframe proved highly practical when the radar rainfall data was entered into the SWMM module at 10-minute intervals. The water level predictions, when applied to the four rainfall scenarios, also aligned within a reasonable range.