## 2 Study Area and Materials
### Hydrological-Topographic Factors
#### 2.2.4 Inducing Factor  
Recently, the frequency of localized torrential rains has been increasing due to climate change [34]. In Seoul, which was affected by this, the number of occurrences of more than 30 mm/h of rainfall increased by 2.3 times throughout the year compared to before 1990, and that of more than 50 mm/h of rainfall increased by 5.3 times [35]. In addition, [PERSON] et al. [35] analyzed that rainfall of 75.0 mm and 15.5 mm/h was observed at the Seodaemun (412) and Dobong (406) observatories in Seoul at 14:00 on 21 September 2010, respectively, showing an approximately 5-fold difference between the two observatories. As such, in terms of the temporal distribution of rainfall, the occurrence frequency of concentrated torrential rains (30 mm/h or more) increases, and the spatial distribution also shows a large deviation due to localized heavy rains. Therefore, it was confirmed that topographic and hydrological factors should be connected when estimating flood vulnerability in this study. Inundation damage in Seoul resulted mainly from inland floods, which occurred in urban lowlands or were caused by rainfall that could overwhelm the drainage infrastructure, rather than fluvial floods [21]. In Korea, when designing drainage pipes to protect against flood, the rainfall duration and the return period generally considered are 1 h and 10-30 years, respectively [36]. Therefore, in this study, maximum 1-h rainfall was used as an inducing factor that causes urban flooding. Korea Meteorological Administration (KMA) [29] provides various types of observation data, such as automated synoptic observing system (ASOS) and automated weather system (AWS), as shown in Table 1. ASOS is installed in the location of the former KMA to perform observation tasks such as observing weather phenomena and data sharing through international cooperation, and AWS is installed in places where observation by a human operator is difficult, such as on mountains or islands, to monitor localized severe weather phenomena in real time [37]. There were a total of 32 rainfall observatories located in and near Seoul, as shown in Table 1, but to secure the reliability of the data required to develop a regression model, it was necessary to select data in consideration of missing observations, and the opening/closure of such observatories. The data from the Gangseo (404) and Gwangjin (413) observatories were excluded because missing data were found at the time of the occurrence of maximum 1-h rainfall observed at a nearby observatory in 2001. Those from Bukaksan (422), Guro (423), Gangbuk (424), and Namhyeon (425) observatories were excluded because they opened after 2001. The selected data were interpolated using the inverse distance weighting (IDW) method to assign the rainfall at the point where the observatories were located to all the relevant grids (Figure 5). On the other hand, inundation trace maps provided information on flooded areas, but did not provide information on the date and time of flooding. If information on the time of flooding is not available, such data cannot be linked to rainfall data. Therefore, inthis study, the maximum 1-h rainfall occurring in July 2001 was used as an independent variable for logistic regression. After that, to evaluate the performance of the regression equation, flood vulnerability was estimated using the maximum 1-h rainfall in September 2010 and July 2011, and compared with the inundation trace maps. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stn. No. **} & \multirow{2}{*}{**Stn. Name**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Start Date of Obs. **} & \multicolumn{4}{c}{**Max.