Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
Source abstract
Abstract
Increasing frequency and severity of urban flooding, driven by climate change and urban population growth, present major challenges. Traditional flood control infrastructure alone cannot fully prevent flood damage, highlighting the need for a comprehensive and multi-dimensional disaster management approach. This study proposes the Flood Risk Index for Building (FRIB)—a building-level assessment framework that integrates vulnerability, hazard, and exposure. FRIB assigns customized risk levels to individual buildings and evaluates the effectiveness of a multi-dimensional method. Compared to traditional indicators like flood depth, FRIB more accurately identifies high-risk areas by incorporating diverse risk factors. It also enables efficient resource allocation by excluding low-risk buildings, focusing efforts on high-risk zones. For example, in a case where 5124 buildings were targeted based on 1 m flood depth, applying FRIB excluded 24 buildings with “low” risk and up to 530 with “high” risk, reducing unnecessary interventions. Moreover, quantitative metrics like entropy and variance showed that as FRIB levels rise, flood depth distributions become more balanced—demonstrating that depth alone does not determine risk. In conclusion, while qualitative labels such as “very low” to “very high” aid intuitive understanding, FRIB’s quantitative, multi-dimensional approach enhances precision in urban flood management. Future research may expand FRIB’s application to varied regions, supporting tailored flood response strategies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Flood Risk Assessment and Management · Evaluation Methods in Various Fields
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil & Environment Engineering, Hongik University, Seoul 04066, Republic of Korea
Funding
Korea Environment Industry & Technology Institute (KEITI)
References (34)
Show source-provided references
1. Strategies to Adapt to an Uncertain Climate Change10.1016/j.gloenvcha.2008.12.003 ↗
2. Li, C., Sun, N., Lu, Y., Guo, B., Wang, Y., Sun, X., and Yao, Y. (2022). Review on urban flood risk assessment. Sustainability, 15.10.3390/su15010765 ↗
3. El-Bagoury, H., and Gad, A. (2024). Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS. Water, 16.10.3390/w16020356 ↗
4. Ueda, F., Tanouchi, H., Egusa, N., and Yoshihiro, T. (2024). A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction. Water, 16.10.3390/w16040607 ↗
5. Flood forecasting in Jhelum river basin using integrated hydrological and hydraulic modeling approach with a real-time updating procedure10.1007/s00382-022-06206-3 ↗
6. Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning10.46604/ijeti.2024.13426 ↗
7. Yin, K., and Mostafavi, A. (2023). Unsupervised graph deep learning reveals emergent flood risk profile of urban areas. arXiv.10.2139/ssrn.4631611 ↗
8. Gigović, L., Pamučar, D., Bajić, Z., and Drobnjak, S. (2017). Application of GIS-Interval Rough AHP Methodology for Flood Hazard Mapping in Urban Areas. Water, 9.10.3390/w9060360 ↗
9. Song, J. (2012). Study on Flood Risk Assessment in Medium and Small River Watersheds for Flood Protection Alternatives. [Ph.D. Thesis, Yeungnam University].
10. Nam, Y. (2015). Study on Identification of Flood Risk Areas by Flood Risk Assessment. [Master’s Thesis, Yeungnam University].
11. Park, K. (2012). Flood Risk Estimation on Gamcheon River Basin Using MCDM. [Master’s Thesis, Kyungpook National University].
12. Integrated Urban Flood Risk Assessment-Adapting a Multicriteria Approach to a City10.5194/nhess-9-1881-2009 ↗
13. Flood Hazard Risk Assessment Model Based on Random Forest10.1016/j.jhydrol.2015.06.008 ↗
14. Zhang, W., Hu, B., Liu, Y., Zhang, X., and Li, Z. (2023). Urban flood risk assessment through the integration of natural and human resilience based on machine learning models. Remote Sens., 15.10.3390/rs15143678 ↗
15. United Nations Office for Disaster Risk Reduction (UNDRR) (2015). Global Assessment Report on Disaster Risk Reduction: Making Development Sustainable: The Future of Disaster Risk Management, UNDRR.
16. Taromideh, F., Fazloula, R., Choubin, B., Emadi, A., and Berndtsson, R. (2022). Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning. Sustainability, 14.10.20944/preprints202201.0133.v1 ↗
17. Ziegelaar, M., and Kuleshov, Y. (2022). Flood Exposure Assessment and Mapping: A Case Study for Australia’s Hawkesbury-Nepean Catchment. Hydrology, 9.10.3390/hydrology9110193 ↗
18. Rincón, D., Khan, U.T., and Armenakis, C. (2018). Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study. Geosciences, 8.10.3390/geosciences8080275 ↗
19. Flood Risk Index Development at the Municipal Level in Costa Rica: A Methodological Framework10.1016/j.envsci.2022.03.012 ↗
20. Schwarz, I., and Kuleshov, Y. (2022). Flood Vulnerability Assessment and Mapping: A Case Study for Australia’s Hawkesbury-Nepean Catchment. Remote Sens., 14.10.3390/rs14194894 ↗
21. Urban Flood Risk Assessment Characterizing the Relationship among Hazard, Exposure, and Vulnerability10.1007/s11356-023-28578-7 ↗
22. Flood Risk Assessment and Mapping Based on a Modified Multi-Parameter Flood Hazard Index Model in the Guanzhong Urban Area, China10.1007/s00477-017-1429-5 ↗
23. Financing Increasing Flood Risk: Evidence from Millions of Buildings
24. National Geographic Information Institute (2024). National Land Information Map Service, National Geographic Information Institute. Available online: https://map.ngii.go.kr/ms/map/NlipMap.do.
25. Korea Institute of Civil Engineering and Building Technology (KICT) (2024). KICT Database Management System, KICT. Available online: https://github.com/floodmodel/K-CDMS/tree/main/Damage%20function.
26. Ministry of the Interior and Safety (2017). Practical Manual for Flood Prevention Standards in Underground Spaces.
27. An Experimental Study on Evacuation Ability During Underground Space Inundation10.9798/KOSHAM.2015.15.2.189 ↗
28. Evaluation of Inundation Risk Ranking for Urban Sewer Systems Using PROMETHEE
29. National Emergency Management Agency (NEMA) (2011). Estimation Method of Safety Index for Community Natural Disaster Considering Climate Change.
30. A Study on Risk Analysis of Social Disaster10.21729/ksds.2016.9.2.15 ↗
31. Risk Ranking among Types of Disaster and Safety Accidents Based on Euclidean Distance Using Damage Statistical Data10.9798/KOSHAM.2018.18.6.133 ↗
32. The Data Model Concept in Statistical Mapping
33. Urban Flood Vulnerability and Risk Assessments for Applying to Urban Planning10.17208/jkpa.2018.10.53.5.185 ↗
34. Development of a Method to Create a Matrix of Heavy Rain Damage Rating Standards Using Rainfall and Heavy Rain Damage Data