## 1 Introduction  
Floods can have several causes, and result mainly from hydro-meteorological causes such as typhoons and localized torrential downpours. Recently, changes in atmospheric flow caused by global warming and climate change have brought about major meteorological problems. In particular, in Northeast Asian regions such as Korea, China, and Japan, atmospheric flow stagnated due to the abnormal high temperatures in the polar regions in the summer of 2020. This led to the longest rainy season ever, causing huge losses. Other causes of flooding include a decrease in the rainwater storage effect of forests due to reckless development, and an increase in impervious areas due to urbanization. In Seoul, Korea, as the Gangnam region began to be developed in earnest after the 1970s, the low-lying areas were newly developed for residential purposes and lost their rainwater storage function [1]. In such densely populated urban areas, the occurrence of flooding will increase further because low-lying areas will be developed to resolve the inadequacy of the supply of housing compared to demand. To reduce the loss caused by frequent floods in recent years, central and local governments have established measures to prevent such flood damage. However, budget limitations mean that not all areas vulnerable to flooding can be refurbished with flood prevention facilities. For this reason, it is important to prioritize relatively more vulnerable areas, and in some cases, information on vulnerable areas should be provided to residents. Providing such information not only improves the ability of residents to cope with floods through education and training, but also has the effect of restraining the development of relevant areas. The advantage in using physically based models is their high capability for prognosis and forecasting, while their disadvantage is the high input data demand [2]. For this reason, techniques for identifying flood vulnerable areas using topographic factors have been suggested in various ways by previous studies. The determination of flood vulnerable areas is one of the representative non-structural measures in flood defense, and should be performed reasonably through hydrological and topographic analysis of rainfall-runoff. As such, techniques for determining flood vulnerable areas have been studied by researchers in a number of fields including hydrology, topography, and soil science. [PERSON] et al. [3] laid the foundation for calibrating or evaluating surface runoff susceptibility mappings through on-site monitoring that measures surface runoff. [PERSON] et al. [4] presented the indicateur du ruissellement intense pluvial (IRIP) technique that maps the characteristics of surfaces that are susceptible to generation, transferal, and accumulation of surface rainfall-runoff. [PERSON] et al. [5] compared the detection rates of flood vulnerability based on topographic factors using general data such as advanced spaceborne thermal emission and reflection radiometer (ASTER) and shuttle radar topography mission (STRM). [PERSON] and [PERSON] [6] analyzed the correlation between topographic factors considering rainfall-runoff characteristics, as well as actual inundation trace data. Flood vulnerability has been estimated using the physical characteristics of the surfaces on which rainfall-runoff are likely to accumulate, such as lowlands and gentle slopes; more recently, studies have been performed that attempt to use machine learning to calculate flood vulnerability. Logistic regression, a field of machine learning, can suggest vulnerability in the study area in a probabilistic manner through binary classification of past data (yes or no) by connecting topographic factors and natural disasters such as floods and landslides [7; 8; 9]. In addition, studies on estimating flood vulnerability using other machine learning techniques are also being conducted by many researchers. Among those, studies using random forests [10; 11] and principal component analysis (PCA) [12; 13] have been actively conducted.