## 4 Results and Discussion
### Discussion  
This study proposed a technique for calculating the flood vulnerability that changes according to the rainfall situation using hydrological-topographic factors. [PERSON] et al. (2016) suggested that studies using globally available data, such as SRTM and ASTER, are needed so that they can be used even in areas where data are insufficient in flood vulnerability analysis. In addition, they said that it is necessary to develop a technique that can evaluate flood vulnerability in a simple but scientific way that can be applied to areas where there are no data on hydrological observations or poor quality. Against this background, in this study, topographic data that can be used anywhere was used as an independent variable of the logistic regression model, and data on soil or land use, which may not be available  
Figure 12: ROC curves of flood vulnerability and inundation traces for performance evaluation: (**a**) September 2010; (**b**) July 2011. Figure 13: Proportion of flood occurrence in vulnerable areas for performance evaluation: (**a**) September 2010; (**b**) July 2011.  
depending on the region, were not added. However, reviewing previous studies, 89% of the floods that occurred in Seoul in 2011 occurred in areas with an impermeability rate of 70% or higher [21]. Further, it was analyzed that 52.1% of the study area consisted of roads, residential, and commercial areas, and 89.4% of floods occurred in these areas. It would be good if soil impermeability or land use is added as an independent variable through future research. In this study, flood vulnerability was calculated using hydrological-topographic factors and compared with historical inundation trace data. As a result, there were some cases where flooding occurred in areas with a calculated vulnerability lower than 0.5, and other cases where flooding did not occur even in areas above 0.5. However, it remains uncertain whether areas with relatively low vulnerability are safer. It is true that areas with high vulnerability require intensive management due to their high probability of flooding, but even areas with low vulnerability should be managed with constant attention to reduce flood damage. Since floods occur for very complicated causes, it may be difficult to detect them using only the factors used in this study. [PERSON] et al. [53] proposed an optimal input data selection method by combining total rainfall, rainfall of various durations, kurtosis, and skewness to predict urban flooding using a deep neural network. If the characteristics of rainfall, such as various durations, kurtosis, and skewness, are considered as an inducing factor, the detection accuracy for flood vulnerable areas can be improved. The flood vulnerability, calculated using hydrological-topographic factors, did not take into account the density and importance of the population and capital in the study area. If a flood occurs in a densely populated area, it is difficult for many people to evacuate all at once, so even if the area has a low vulnerability, it is necessary to pay close attention to the area. Similarly, in areas where major social overhead capital (SOC) facilities are located, such as power plants or water supply/ wastewater treatment facilities, great damage can be caused to surrounding areas when floods occur. [PERSON] et al. [54] reviewed scholarly articles related to flood vulnerability from 1990 to 2018, noting that flood vulnerability is being analyzed in social, environmental, and economic contexts, and presented a list of indicators that can be used for future research. In this regard, it is necessary to distinguish the vulnerability of regions with high socio-economic vulnerability from the criteria for calculating flood vulnerability ratings for other regions. Another point to be improved in the methodology of this study is that the vulnerability of the entire study area was calculated using one logistic regression equation.