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Simulation of Urban Environments for Disaster Risk Management: Comprehensive Review of Techniques and Future Directions

  • Kieun LEE (Department of Civil Systems Engineering, College of Engineering, Ajou University) ;
  • Taeyong KIM (Department of Civil Systems Engineering, College of Engineering, Ajou University) ;
  • Sungkon MOON (Department of Civil Systems Engineering, College of Engineering, Ajou University)
  • 발행 : 2024.07.29

초록

As cities continue to evolve and expand, the importance of accurately modeling and simulating urban environments to predict and assess various risk scenarios has become increasingly recognized. Since city simulation can capture the intricate dynamics of urban life, the versatility of city simulation has been demonstrated in numerous case studies across diverse applications. Owing to this capacity, city simulation plays a critical role in the disaster risk management field, especially in accounting for the uncertainties in natural/man-made disasters. For example, in the event of an earthquake, having detailed information about an urban area is instrumental for evaluating stakeholder decisions and their impact on urban recovery strategies. Although numerous research efforts have been made to introduce city simulation techniques in disaster risk reduction, there is no clear guideline or comprehensive summary of their characteristics and features. Therefore, this study aims to provide a high-level overview of the latest research and advancements in urban simulation under different hazards. The study begins by examining the simulation techniques used in urban simulation, with a focus on their applicability in disaster scenarios. Subsequently, by analyzing various case studies, this research categorizes them based on their unique characteristics and key findings. The knowledge gained from this literature review will serve as a foundation for subsequent research on simulating the impacts of urban areas under various hazards.

키워드

과제정보

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant Number: 2022R1F1A1074039)

참고문헌

  1. Ogawa, Y., Y. Sekimoto, and R. Shibasaki, Estimation of earthquake damage to urban environments using sparse modeling. Environment and Planning B: Urban Analytics and City Science, 2021. 48(5): p. 1075-1090.
  2. Yun, T.-S., et al. Agent-based modeling and simulation on residential population movement patterns: the case of Sejong city. in 2020 Winter Simulation Conference (WSC). 2020. IEEE.
  3. Liu, G., S. Chen, and J. Gu, Urban renewal simulation with spatial, economic and policy dynamics: The rent-gap theory-based model and the case study of Chongqing. Land Use Policy, 2019. 86: p. 238-252.
  4. Bihamta, N., et al., Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan metropolitan area, Iran. Journal of the Indian Society of Remote Sensing, 2015. 43: p. 407-414.
  5. Gao, L., et al., Multi-scenario simulation and ecological risk analysis of land use based on the PLUS model: A case study of Nanjing. Sustainable Cities and Society, 2022. 85: p. 104055.
  6. Xing, L., M. Xue, and M. Hu, Dynamic simulation and assessment of the coupling coordination degree of the economy-resource-environment system: Case of Wuhan City in China. Journal of Environmental Management, 2019. 230: p. 474-487.
  7. Jiang, W., et al., Toward Interoperable Multi-Hazard Modeling: A Disaster Management System for Disaster Model Service Chain. International Journal of Disaster Risk Science, 2022. 13(6): p. 862-877.
  8. Zlatanova, S., SII for emergency response: the 3D challenges. J. Chen, J. Jiang and S. Nayak (Eds.), 2008: p. 1631-1637.
  9. Caglayan, N. and S.I. Satoglu, Simulation analysis of critical factors of casualty transportation for disaster response: A case study of Istanbul earthquake. International Journal of Disaster Resilience in the Built Environment, 2022. 13(5): p. 632-647.
  10. Liu, Y., et al., Large-scale natural disaster risk scenario analysis: a case study of Wenzhou City, China. Natural hazards, 2012. 60: p. 1287-1298.
  11. Shi, Y., et al., How can cities respond to flood disaster risks under multi-scenario simulation? A case study of Xiamen, China. International journal of environmental research and public health, 2019. 16(4): p. 618.
  12. Herman, S., Disaster Scene Reconstruction: Modeling, Simulating, And Planning In An Urban Disaster Environment. 2014.
  13. Gundran, C.P.D., et al., Simulation training needs assessment for disaster preparedness and disaster response among selected agencies in national capital region, Philippines. International Journal of Disaster Risk Reduction, 2023: p. 103824.
  14. Hemadasa, N., et al. DiNS: Nature Disaster in Network Simulations. in 2022 18th International Conference on Mobility, Sensing and Networking (MSN). 2022. IEEE.
  15. Puno, G., et al., Mapping and analysis of flood scenarios using numerical models and GIS techniques. Spatial Information Research, 2020. 28(2): p. 215-226.
  16. Rodrigues da Silva, A., et al., A Web GIS Platform to Modeling, Simulate and Analyze Flood Events: The RiverCure Portal. ISPRS International Journal of Geo-Information, 2023. 12(7): p. 268.
  17. Lee, S., et al., Dynamic-data-driven agent-based modeling for the prediction of evacuation behavior during hurricanes. Simulation Modelling Practice and Theory, 2021. 106: p. 102193.
  18. Feizizadeh, B., S.J. Adabikhosh, and S. Panahi, A Scenario-Based and Game-Based Geographical Information System (GIS) Approach for Earthquake Disaster Simulation and Crisis Mitigation. Sustainability, 2023. 15(14): p. 11131.
  19. Yin, W., et al., An agent-based modeling system for travel demand simulation for hurricane evacuation. Transportation research part C: emerging technologies, 2014. 42: p. 44-59.
  20. Taillandier, F., et al., An agent-based model to simulate inhabitants' behavior during a flood event. International Journal of Disaster Risk Reduction, 2021. 64: p. 102503.
  21. Vandewalle, R., et al. Integrating CyberGIS-Jupyter and spatial agent-based modelling to evaluate emergency evacuation time. in proceedings of the 2nd ACM SIGSPATIAL international workshop on GeoSpatial simulation. 2019.
  22. Maqbool, A., et al., Disaster mitigation in Urban Pakistan using agent based modeling with GIS. ISPRS International Journal of Geo-Information, 2020. 9(4): p. 203.
  23. Liu, Y., et al., Intelligent prediction method for waterlogging risk based on AI and numerical model. Water, 2022. 14(15): p. 2282.
  24. Fakhrzad, M. and H. Hasanzadeh, A new mathematical modelling for relief operation based on stochastic programming. International Journal of Process Management and Benchmarking, 2020. 10(2): p. 224-239.
  25. Dong, B., et al., Experimental and numerical model studies on flash flood inundation processes over a typical urban street. Advances in Water Resources, 2021. 147: p. 103824.
  26. Chang, T.-Y., et al., An operational high-performance forecasting system for city-scale pluvial flash floods in the southwestern plain areas of Taiwan. Water, 2021. 13(4): p. 405.
  27. Goodchild, M., Twenty years of progress: GIScience in 2010. Journal of spatial information science, 2010(1): p. 3-20.
  28. Khan, S.M., et al., A systematic review of disaster management systems: approaches, challenges, and future directions. Land, 2023. 12(8): p. 1514.
  29. GANG, S.-M., et al., A plan for a prompt disaster response system using a 3D disaster management system based on high-capacity geographic and disaster information. Journal of the Korean Association of Geographic Information Studies, 2016. 19(1): p. 180-196.
  30. Schneider, P.J. and B.A. Schauer, HAZUS-Its development and its future. Natural Hazards Review, 2006. 7(2): p. 40-44.
  31. Asim, K.M., et al., Seismic activity prediction using computational intelligence techniques in northern Pakistan. Acta Geophysica, 2017. 65: p. 919-930.
  32. Tehseen, R., M.S. Farooq, and A. Abid, Earthquake prediction using expert systems: a systematic mapping study. Sustainability, 2020. 12(6): p. 2420.
  33. Zhang, L., Z. Tao, and G. Wang, Assessment and determination of earthquake casualty gathering area based on building damage state and spatial characteristics analysis. International Journal of Disaster Risk Reduction, 2022. 67: p. 102688.
  34. Hosseinzadeh-Tabrizi, S.A., M. Ghaeini-Hessaroeyeh, and M. Ziaadini-Dashtekhaki, Numerical simulation of dam-breach flood waves. Applied Water Science, 2022. 12(5): p. 100.
  35. Gharehbaghi, S., et al., Prediction of seismic damage spectra using computational intelligence methods. Computers & Structures, 2021. 253: p. 106584.
  36. Marasco, S., et al., A computational framework for large-scale seismic simulations of residential building stock. Engineering Structures, 2021. 244: p. 112690.
  37. Patel, D.P. and P.K. Srivastava, Flood hazards mitigation analysis using remote sensing and GIS: correspondence with town planning scheme. Water resources management, 2013. 27(7): p. 2353-2368.
  38. Li, W., et al., A rapid 3D reproduction system of dam-break floods constrained by post-disaster information. Environmental Modelling & Software, 2021. 139: p. 104994.
  39. Rangari, V.A., N. Umamahesh, and C. Bhatt, Assessment of inundation risk in urban floods using HEC RAS 2D. Modeling Earth Systems and Environment, 2019. 5: p. 1839-1851.
  40. Feng, B., Y. Zhang, and R. Bourke, Urbanization impacts on flood risks based on urban growth data and coupled flood models. Natural Hazards, 2021. 106: p. 613-627.
  41. Rong, Y., et al., Three-dimensional urban flood inundation simulation based on digital aerial photogrammetry. Journal of Hydrology, 2020. 584: p. 124308.
  42. Gioia, E. and F. Marincioni, Leaving Nothing to Chance: Reducing Flood Risk by Evaluating Simulation Exercises in Urban Contexts, in Disaster Resilience and Human Settlements: Emerging Perspectives in the Anthropocene. 2023, Springer. p. 21-43.
  43. Bernardini, G., et al., Assessing the flood risk to evacuees in outdoor built environments and relative risk reduction strategies. International Journal of Disaster Risk Reduction, 2021. 64: p. 102493.
  44. Fathianpour, A., et al., Tsunami evacuation modelling via micro-simulation model. Progress in Disaster Science, 2023. 17: p. 100277.
  45. Sobhani, B. and V.S. Zengir, Modeling, monitoring and forecasting of drought in south and southwestern Iran, Iran. Modeling Earth Systems and Environment, 2020. 6: p. 63-71.
  46. Luo, Z., et al., Impact of urbanization factors considering artificial water dissipation on extreme precipitation: A numerical simulation of rainfall in Shanghai. Quarterly Journal of the Royal Meteorological Society, 2023. 149(755): p. 2320-2332.
  47. Mutthulakshmi, K., et al., Simulating forest fire spread and fire-fighting using cellular automata. Chinese Journal of Physics, 2020. 65: p. 642-650.
  48. Li, W., et al., Future changes in the frequency of extreme droughts over China based on two large ensemble simulations. Journal of Climate, 2021. 34(14): p. 6023-6035.
  49. Shi, X., et al., Simulation of storm surge inundation under different typhoon intensity scenarios: case study of Pingyang County, China. Natural Hazards and Earth System Sciences, 2020. 20(10): p. 2777-2790.
  50. Wang, Z. and G. Jia, A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural hazards, 2021. 105: p. 2045-2071.
  51. Roy, K.C., et al., Understanding the influence of multiple information sources on risk perception dynamics and evacuation decisions: An agent-based modeling approach. International Journal of Disaster Risk Reduction, 2022. 82: p. 103328.
  52. Wang, F., et al., Numerical simulation of damage behaviour of building sandstone exposed to fire. Rock Mechanics and Rock Engineering, 2021. 54: p. 3149-3164.
  53. Gong, X. and A.K. Agrawal, Numerical simulation of fire damage to a long-span truss bridge. Journal of bridge Engineering, 2015. 20(10): p. 04014109.
  54. Efendi, A.W., Simulation of fire exposure behavior to building structural elements using LISA FEA V. 8. International Journal of Advanced Science and Computer Applications, 2023. 2(2): p. 49-58.
  55. Provitolo, D., R. Lozi, and E. Tric, Topological analysis of a weighted human behaviour model coupled on a street and place network in the context of urban terrorist attacks. Mathematical Modelling, Optimization, Analytic and Numerical Solutions, 2020: p. 117-146.
  56. Stoltz, E.M., J.K. Clutter, and F. Hudson, Modeling property value loss in a city due to terrorist bombings. IEEE Systems Journal, 2009. 3(2): p. 221-230.
  57. Tang, Z., et al., Risk analysis of urban dirty bomb attacking based on Bayesian network. Sustainability, 2019. 11(2): p. 306.
  58. Quagliarini, E., G. Bernardini, and M. D'Orazio, How Could Increasing Temperature Scenarios Alter the Risk of Terrorist Acts in Different Historical Squares? A Simulation-Based Approach in Typological Italian Squares. Heritage, 2023. 6(7): p. 5151-5186.