Abstract
In this study, fuzzy mathematics and VIKOR were employed to develop a flood vulnerability assessment system for Ulsan Metropolitan City, South Korea in 2018. HEC-HMS model was used to simulate the major rivers’ runoff in Ulsan Metropolitan City, and HEC-RAS model was used to convert the 1-D runoff simulation results into 2-D results of inundation map, while the simulation results were exhibited through ArcMap. Hazards, sensitivity and adaptivity were selected as the three evaluation indicators for fuzzy comprehensive evaluation, and pressure resilience, state resilience and response resilience were utilized as the indicators for ranking and comparison of the VIKOR method for local governments (administrative districts) of Ulsan Metropolitan City. As the result, Dong-gu district, Ulju-jun County and Nam-gu district showed the best results by both the fuzzy mathematical and the VIKOR method in hazard and pressure, sensitivity and state, adaptivity and response indicator. Fuzzy mathematics and the VIKOR method provide two different ways to study the flood protection capacity of Ulsan Metropolitan City, comparing the historical statistics, VIKOR method’s evaluation results match well with the flooding statistics in Ulsan Metropolitan City.
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References
Adger WN (2006) Vulnerability. Global Environmental Change 16(3): 268–281, DOI: https://doi.org/10.1016/j.gloenvcha.2006.02.006
Ahmad SS, Simonovic SP (2013) Spatial and temporal analysis of urban flood risk assessment. Urban Water Journal 10(1):26–49, DOI: https://doi.org/10.1080/1573062X.2012.690437
Alcantara AL, Ahn K (2021) Future flood riverine risk analysis considering the heterogeneous impacts from tropical cyclone and non-tropical cyclone rainfalls: Application to daily flows in the Nam River Basin, South Korea. Advances in Water Resources 154:103983, DOI: https://doi.org/10.1016/j.advwatres.2021.103983
Bae S, Chang H (2019) Urbanization and floods in the Seoul Metropolitan area of South Korea: What old maps tell us. International Journal of Disaster Risk Reduction 37:101186, DOI: https://doi.org/10.1016/j.ijdrr.2019.101186
Bera B, Shit PK, Sengupta N, Saha S, Bhattacharjee S (2022) Susceptibility of deforestation hotspots in Terai-Dooars belt of Himalayan Foothills: A comparative analysis of VIKOR and TOPSIS models. Journal of King Saud University - Computer and Information Sciences 34(10):8794–8806, DOI: https://doi.org/10.1016/j.jksico.2021.10.005
Chang L, Huang S (2015) Assessing urban flooding vulnerability with an emergy approach. Landscape and Urban Planning 143:11–24, DOI: https://doi.org/10.1016/j.landurbplan.2015.06.004
Chen S, Lin Y (2009) On performance evaluation of ERP systems with fuzzy mathematics. Expert Systems with Applications 36(3):6362–6367, DOI: https://doi.org/10.1016/j.eswa.2008.08.078
Cuomo A, Guida D (2021) Hydro-geomorphologic-based water budget at event time-scale in a mediterranean headwater catchment (Southern Italy). Hydrology 8(1):20, DOI: https://doi.org/10.3390/hydrology8010020
Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Klove B (2019) Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of Hydrology 569:142–154, DOI: https://doi.org/10.1016/j.jhydrol.2018.12.002
Desalegn H, Mulu A (2021) Mapping flood inundation areas using GIS and HEC-RAS model at Fetam River, Upper Abbay Basin, Ethiopia. Scientific African 12:e00834, DOI: https://doi.org/10.1016/j.sciaf.2021.e00834
Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction 50:101687, DOI: https://doi.org/10.1016/j.ijdrr.2020.101687
França LCDJ, Lopes LF, Morais MSD, Lisboa GDS, Rocha SJSSD, Junior VTMDM, Santana RC, Mucida DP (2022) Environmental fragility zoning using GIS and AHP modeling: Perspectives for the conservation of natural ecosystems in Brazil. Conservation 2(2): 349–366, DOI: https://doi.org/10.3390/conservation2020024
Gugliotta M, Saito Y (2019) Matching trends in channel width, sinuosity, and depth along the fluvial to marine transition zone of tide-dominated river deltas: The need for a revision of depositional and hydraulic models. Earth-Science Reviews 191:93–113, DOI: https://doi.org/10.1016/j.earscirev.2019.02.002
Hadi ZN, Almansori NJHA (2021) Estimation of Manning coefficient for the section between Al-Hindiya barrage and Al-Kufa barrage utilizing HEC-RAS. Materials Today: Proceedings DOI: https://doi.org/10.1016/j.matpr.2021.06.417
Han D, Kim J, Choi C, Han H, Necesito IV (2021) Case study: On hydrological function improvement for an endemic plant habitat in Gangcheon wetland, Korea. Ecological Engineering 160:106028, DOI: https://doi.org/10.1016/j.ecoleng.2020.106028
Han H, Kim D, Kim HS (2022) Inundation analysis of coastal urban area under climate change scenarios. Water 14(7):1159, DOI: https://doi.org/10.3390/w14071159
Hu S, Cheng X, Zhou D, Zhang H (2017) GIS-based flood risk assessment in suburban areas: A case study of the Fangshan District, Beijing. Natural Hazards 87(3):1525–1543, DOI: https://doi.org/10.1007/s11069-017-2828-0
Kim D, Choi C, Kim J, Lee J, Bae Y, Kim HS (2018) Analysis of heavy rain damage considering regional characteristics. Journal of the Korean Society of Hazard Mitigation 18(4):311–320, DOI: https://doi.org/10.9798/KOSHAM.2018.18.4.311
Kim K, Han D, Kim D, Wang W, Jung J, Kim J, Kim HS (2019a) Combination of structural measures for flood prevention in Anyangcheon River Basin, South Korea. Water (Basel) 11(11):2268, DOI: https://doi.org/10.3390/w11112268
Kim D, Han H, Wang W, Kim HS (2022a) Improvement of deep learning models for river water level prediction using complex network method. Water 14(3):466, DOI: https://doi.org/10.3390/w14030466
Kim D, Kim J, Choi, C, Wang W, You Y, Kim HS (2019b) Estimations of hazard-triggering rainfall and breach discharge of aging reservoir. Journal of the Korean Society of Hazard Mitigation 19(7):421432, DOI: https://doi.org/10.9798/KOSHAM.2019.19.7.421
Kim J, Kim JG, Jung J, Han D, Choi C, Kim HS (2020) Modified hydrogeomorphic approach for estimating quantitative change of riverine wetland functions. Ecological Engineering 152:105876, DOI: https://doi.org/10.3390/rs13214381
Kim J, Kim D, Lee M, Han H, Kim HS (2022b) Determining the risk level of heavy rain damage by region in South Korea. Water 14(2): 219, DOI: https://doi.org/10.3390/w14020219
Lee H, Alday JG, Cho K, Lee EJ, Marrs RH (2014) Effects of flooding on the seed bank and soil properties in a conservation area on the Han River, South Korea. Ecological Engineering 70:102–113, DOI: https://doi.org/10.1016/j.ecoleng.2014.04.014
Lee D, Shin J, Song Y, Chang H, Cho H, Park J, Hong J (2022) The development process and significance of the 3rd national climate change adaptation plan (2021 - 2025) of the Republic of Korea. Science of The Total Environment 818:151728, DOI: https://doi.org/10.1016/j.scitotenv.2021.151728
Luo Z, Shao Q, Zuo Q, Cui Y (2020) Impact of land use and urbanization on river water quality and ecology in a dam dominated basin. Journal of Hydrology 584:124655, DOI: https://doi.org/10.1016/j.jhydrol.2020.124655
Luo Y, Zhang J, Yu M, Liang X, Xia R, Gao Y, Gao X, Yin J (2023) On the influences of urbanization on the extreme rainfall over zhengzhou on 20 July 2021: A convection-permitting ensemble modeling study. Advances in Atmospheric Sciences 40(3):393–409, DOI: https://doi.org/10.1007/s00376-022-2048-8
Lv H, Guan X, Meng Y (2020) Comprehensive evaluation of urban flood-bearing risks based on combined compound fuzzy matterelement and entropy weight model. Natural Hazards 103(2):1823–1841, DOI: https://doi.org/10.1007/s11069-020-04056-y
Mark O, Jorgensen C, Hammond M, Khan D, Tjener R, Erichsen A, Helwigh B (2018) A new methodology for modelling of health risk from urban flooding exemplified by cholera - case Dhaka, Bangladesh. Flood Risk Management 11:S28–S42, DOI: https://doi.org/10.1111/jfr3.12182
Opricovic S, Tzeng G (2004) Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 156(2):445–455, DOI: https://doi.org/10.1016/S0377-2217(03)00020-1
Oleyiblo JO, Li Z (2010) Application of HEC-HMS for flood forecasting in Misai and Wan’ an catchments in China. Water Science and Engineering 3(1):14–22, DOI: https://doi.org/10.3882/j.issn.1674-2370.2010.01.002
Ro B, Garfin G (2023) Building urban flood resilience through institutional adaptive capacity: A case study of Seoul, South Korea. International Journal of Disaster Risk Reduction 85:103474, DOI: https://doi.org/10.1016/j.ijdrr.2022.103474
Saaty TL (2004) Decision making - The analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering 13:1–35, DOI: https://doi.org/10.1007/s11518-006-0151-5
Tamiru H, Dinka MO (2021) Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia. Journal of Hydrology: Regional Studies 36:100855, DOI: https://doi.org/10.1016/j.ejrh.2021.100855
Tingsanchali T (2012) Urban flood disaster management. Procedia Engineering 32:25–37, DOI: https://doi.org/10.1016/j.proeng.2012.01.1233
Tu J, Wan M, Chen Y, Tan L, Wang J (2022) Biodiversity assessment in the near-shore waters of Tianjin City, China based on the Pressure-State-Response (PSR) method. Marine Pollution Bulletin 184: 114123, DOI: https://doi.org/10.1016/j.marpolbul.2022.114123
Wu Z, Zhou Y, Wang H, Jiang Z (2020) Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Science of The Total Environment 716:137077, DOI: https://doi.org/10.1016/j.scitotenv.2020.137077
Yan WY, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: A review. Remote Sens Environ 158:295–310, DOI: https://doi.org/10.1016/j.rse.2014.11.001
Zadeh LA (1965) Fuzzy sets. Information and Control 8:338–353, DOI: https://doi.org/10.21236/ad0608981
Zeiger SJ, Hubbart JA (2021) Measuring and modeling event-based environmental flows: An assessment of HEC-RAS 2D rain-on-grid simulations. Journal of Environmental Management 285:112125, DOI: https://doi.org/10.1016/j.jenvman.2021.112125
Zhao L, Zhang T, Fu J, Li J, Cao Z, Feng P (2021) Risk assessment of urban floods based on a SWMM-MIKE21-coupled model using GF-2 data. Remote Sensing 13(21):4381, DOI: https://doi.org/10.1016/j.ecoleng.2020.105876
Zhu R, Liang Q, Zhan H (2017) Analysis of aero-engine performance and selection based on fuzzy comprehensive evaluation. Procedia Engineering 174:1202–1207, DOI: https://doi.org/10.1016/j.proeng.2017.01.283
Zou Z, Yun Y, Sun J (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences 18(5):1020–1023, DOI: https://doi.org/10.1016/s1001-0742(06)60032-6
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Feng, Q., Kim, D., Wang, Wj. et al. A Case Study: Evaluation of Urban Flood Resilience Based on Fuzzy Mathematics and VIKOR Method in Ulsan Metropolitan City, South Korea. KSCE J Civ Eng 28, 1554–1565 (2024). https://doi.org/10.1007/s12205-024-0595-5
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DOI: https://doi.org/10.1007/s12205-024-0595-5