# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 1 Introduction  
As the intensity of heavy rainfall increases due to climate change, the damages caused by urban flood in densely populated areas are progressively escalating. Urban flood poses significant risks to human lives, infrastructure, and economic stability, with its impacts intensifying as extreme weather events become more frequent. The rising intensity of rainfall indicates that the effectiveness of existing structural measures--designed using historical rainfall data--is steadily diminishing. While these issues can be mitigated by adopting higher design frequencies or increasing the height of levees, such approaches often require substantial financial investment and time, as they involve extensive modification of existing infrastructure ([PERSON] et al. [1]). In response, non-structural approaches have gained prominence as alternatives, aiming to maximize the utility of current flood control facilities while proactively identifying hazardous areas and applying localized flood mitigation measures. Among these, GIS, remote sensing, and Multi-Criteria Decision Analysis (MCDA) are representative methodsthat can establish more localized and effective flood management strategies, especially in urban environments with limited data availability ([PERSON], [PERSON], et al. [2]). This method avoids large-scale infrastructure projects, offering cost-effectiveness and reduced implementation timelines as measures are targeted at specific risk-prone areas. However, within this framework, there are two distinct approaches to risk assessment: one relies solely on simple physical indicators, such as flood depth, while the other integrates a wide variety of indicators for a comprehensive risk assessment. The physical-indicator-based approach is intuitive and straightforward, making it widely used for identifying risk areas. Various methods, such as approach through numerical analysis, machine-learning-based approaches, and fuzzy-logic-based approaches, have utilized physical indicators like flood depth. It has been adopted by numerous studies as a traditional method for flood risk assessment ([PERSON] et al. [3]; [PERSON] et al. [4]; [PERSON] et al. [5]; [PERSON] et al. [6]). However, this approach primarily considers the magnitude of flood depth, often failing to account for critical factors like population density, proximity to essential infrastructure, or socio-economic conditions. This limitation has been shown to result in an inability to capture novel risk profiles that emerge due to network vulnerabilities in urban systems ([PERSON], [PERSON], et al. [7]). As a result, its ability to capture the multi-dimensional nature of flood risk is inherently limited. Conversely, comprehensive risk assessments incorporate multiple indicators, such as vulnerability, hazard, and exposure, enabling a more precise evaluation of overall flood risk. This approach leverages diverse datasets to identify high-risk areas while considering socio-environmental factors that influence flood impacts. Many studies have demonstrated the effectiveness of this methodology in producing balanced risk profiles and guiding targeted disaster management strategies ([PERSON] et al. [8]; [PERSON], [9]; [PERSON], [10]; [PERSON], [11]; [PERSON] et al. [12]; [PERSON] et al. [13]). It has also been emphasized that a multidimensional assessment integrating human resilience and environmental exposure performs significantly better than single-variable approaches in urban flood forecasting ([PERSON] et al. [14]). However, the applicability of such methods is often constrained by the availability and accessibility of region-specific datasets, limiting their use in areas like Korea where public data is often generalized or fragmented. Additionally, comparative analyses between the physical-indicator-based approach and comprehensive methods remain limited, leaving a gap in understanding the differences and relative advantages of these two approaches.