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As data analysis becomes increasingly important in the field of flood disasters, a data-based analytical model that considers hydraulic factors has emerged as an alternative to flood analysis. Data-driven modeling requires complex and extensive data that depend on actual and diverse hypothetical scenarios. Thus, preliminary data processing is required to utilize extensive input data efficiently and obtain accurate results. In this study, a gamma test was applied as a preliminary data processing technique to the efficient prediction of flood disasters in urban areas. Specifically, probability rainfall was calculated for the Daejeo and Dadae districts in Busan, and manhole overflow was estimated by inputting relevant data into a storm water management model (SWMM) with a Huff distribution. A preliminary uncertainty and sensitivity analysis was performed with the gamma test using combined time-series data for probability rainfall and manhole overflow, and then an optimal combination of data was selected for a simulation using correlativity analysis (Gamma, Gradient, Standard error, and V-ratio). The proposed method employs quantitative criteria for efficient selection of optimal input data, thus saving a greater amount of time and effort than when using the traditional method that derives results from repeated execution of models.

Uncertainty Analysis of Rainfall Scenarios for the Prediction of Flood Disasters in Urban Areas