## 5 Conclusions  
In this study, we proposed a technique to detect flood vulnerable areas by simultaneously considering topographic and hydrological factors to reduce damage caused by flooding. To estimate the vulnerability to flooding of the study area, a logistic regression model was developed using historical inundation trace data, and hydrological-topographic factors based on the grid system. The conclusions obtained through this study are as follows.  
1. A logistic regression model was established by dividing into a model that only considered topographic factors (T) and one that included hydrological factors (TR), and the results were compared. When comparing the two models, it was found that the estimated result was different due to the influence of rainfall. In addition,according to the results of ROC analysis and precision calculation, it was found that the method of estimating the flood vulnerability that included the hydrological factor was relatively better for detecting the flood occurrence pattern.
2. Flood events in 2010 and 2011 were applied to evaluate the performance of the developed logistic regression model. Through ROC analysis with inundation trace maps, it was found that the AUC improved from 0.841 (T) to 0.861 (TR) in 2010, and from 0.766 (T) to 0.815 (TR) in 2011, indicating that the model including hydrological factors was better for detecting flooding patterns. In addition, according to the actual flood occurrence rates calculated in the vulnerable areas determined in consideration of the rainfall, the detection rate was significantly improved compared to the approach that considered only topographical factors, which overestimated vulnerable areas.
3. There were some cases in which flooding occurred in areas with low vulnerability, while in other cases, high vulnerability areas saw no floods occur. The cause of a flood is very complicated because the factors influence the events in a complex manner through interaction. Therefore, constant attention and management is required even for areas with low vulnerability. In particular, areas with many residents or important SOC facilities should be managed separately.
4. The technique for determining flood vulnerable areas proposed in this study enables an analysis to be performed quickly and conveniently because the topographic factors are fixed in the logistic regression equation and only new rainfall is inputted to it. If real-time rainfall forecasting is available, it will allow us to quickly and easily predict areas that are likely to be flooded. In addition, through further research, if a regression model for each drainage section is developed by subdividing the characteristics of flood damage factors, it is expected that the detection rates will improve and more reliable flood information will be provided.  
Conceptualization, J.-Y.L. and J.-S.K.; methodology, J.-Y.L. and J.-S.K.; software and validation, J.-Y.L.; writing--original draft preparation, J.-Y.L.; writing--review and editing, J.-S.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a Grant (127568) from the Water Management Research Program funded by Ministry of Environment of Korean government.  
Not applicable.  
This study was supported by a Grant (127568) from the Water Management Research Program funded by Ministry of Environment of Korean government.  
The authors declare no conflict of interest.