## 1 Introduction
In addition to studies that applied a single technique, studies which compare or connect several techniques have also been conducted. [PERSON] and [PERSON] [14] compared and proposed methods of detecting landslide-prone areas with logistic regression and artificial neural network (ANN). [PERSON] et al. [15] compared flood vulnerability estimated using random forests and boosted trees with topographic factors as input data. [PERSON] et al. [16] used logistic regression, Naive Bayes, AdaBoost, and random forests to estimate flood vulnerability around the world, and compared the detection capabilities for each model. To reduce the dimensions of various topographic factors, studies on applying logistic regression after PCA [17; 18; 19] have also been conducted. KICT [20] stated that it was necessary to establish special measures for areas prone to flooding and strengthen flood forecast warning systems, in order to respond to floods. [PERSON] and [PERSON] [21] mentioned that the floods that occurred in Seoul in 2010 and 2011 had a high spatial correlation, and that they occurred repeatedly in the same area. In particular, it was analyzed that one-third of the areas which flooded in 2011 were areas that had previously suffered from floods [21]. On this basis, this study confirmed that flood vulnerable areas should be determined through an analysis of the topographical causes of areas where floods frequently occur in Seoul, the study area, and intensively managed. A variety of approaches have been conducted to identify flood vulnerable areas, and the most representative of them is the method using the numerical models [22; 23; 24]. This method is to designate an expected flooding area by calculating the hydraulic-hydrological characteristics of rainfall-runoff for a hypothetical scenario precipitation with a numerical model. Although numerical models showed great capabilities for predicting a diverse range of flooding scenarios, they often require various types of hydro-geomorphological monitoring datasets, requiring intensive computation, which prohibits short-term prediction [25]. Previous studies have suggested data-based techniques for determining flood vulnerable areas using hydrological-topographic factors due to the efficiency of data collection and analysis. However, these methods only calculate the flood vulnerability at the planning level, and do not detect floods for various actual events. To supplement this, in this study, a logistic regression model estimating flood vulnerability that changes according to rainfall was developed and the detection performance was evaluated with a new event. In spatial data-based flood vulnerability analysis, it is important to select input data that can affect floods and collect data. The input data were selected by referring to the topographical factors mainly used in the previous studies [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] introduced above (slope, elevation, topographic wetness index, curvature, stream power index, distance from river, in order of most use). Meanwhile, in Korea, hydrological-topographic data can be easily obtained through the websites [26; 27; 28; 29] of government agencies. These data can be regarded as reliable data because they are produced with strict quality control. The purpose of this study is to develop a technique for determining flood vulnerable areas in order to reduce the damage caused by flooding. As shown in Figure 1, this technique can calculate flood vulnerability by estimating logistic regression coefficients taking into account the hydrological-topographic factors in the study area. This methodology can map flood vulnerable areas suitable for each flood event by changing the values according to the rainfall situation. With this, if real-time rainfall forecasting is used, flooding can be predicted.