# Socioeconomic Impact on Urban Resilience against Flood Damage
## 4 Discussion and Conclusions  
This study assessed urban resilience, considering socioeconomic cost due to urban flood damage. For this purpose, a comprehensive dataset including flood depth, building information, road condition, population, and vehicle data was built and used to verify the assessment. The evaluation formulas for resilience attributes and a socioeconomic evaluation methodology are presented to calculate the resilience of each city, respectively, and the socioeconomic damage due to flooding quantitatively, and the following characteristics were identified:  
1. In the event of a flood, the resilience of each city varies, which is primarily due to the structural characteristics of the cities and their evacuation and response capabilities. For example, despite having similar levels of facility damage (SI), there was a significant difference in evacuation capability (RA or SR), which was expected to indicate critical disparities in the damage and recovery process due to flooding. 2. Each attribute value of resilience against flooding and socioeconomic damage is greatly influenced by the structural characteristics and social capabilities. In particular, it was analyzed that even if similar resilience outcomes are derived, socioeconomic damage can vary depending on the specific attribute values. For instance, the capacity to evacuate, such as RA, and regional population sizes influenced the scale of socioeconomic cost differently on the final value of urban resilience. 3. With the economic analysis, the expected benefits and costs of PSHP were estimated including the operation and maintenance. Urban resilience would indicate a foundational measure for establishing response strategies for the rapid recovery of cities after floods. The analysis method for each attribute value of resilience and socioeconomic cost in this study can provide useful information to decision-makers and stakeholders, contributing to minimizing and mitigating damage caused by floods and enhancing resilience. In conclusion, this study has provided a comprehensive understanding of urban flood resilience and socioeconomic cost assessment. The correlation analysis further supports these findings, revealing that all variables exhibited negative relationships. Notably, a stronger negative correlation was observed between resilience (R) and labor loss (PL) at \(-0.498\), compared to the \(-0.355\) correlation between resilience and traffic congestion costs (TCC). This indicates that higher resilience tends to reduce social costs due to flood damage, with PL being more sensitive to changes in resilience than TCC. This observation can be attributed to the network structure's impact on TCC, where the location of the flood significantly affects traffic congestion differently than labor loss. The analysis highlights the importance of not only structural protection measures but also enhancing social response capabilities and devising strategies to minimize and mitigate impact when developing flood preparedness and response strategies. However, this study did not consider the weight of each attribute during the resilience assessment and did not quantitatively analyze the impact on resilience changes after recovery due to the inability to set priorities for restoration and rehabilitation. Therefore, it is essential to establish a detailed resilience assessment that considers the weight of each attribute and risk assessment techniques for future recovery prioritization. Analyzing resilience changes under various scenarios with risk-based recovery will aid in developing efficient flood mitigation and effective response strategies.