A Quantitative Urban Risk Assessment Based on the Integrated IRI, CFI, and CSI Models

Yuxiang Huang; Kun Zhao; Yi Lin

doi:10.54097/nt034d11

Authors

Yuxiang Huang
Kun Zhao
Yi Lin

DOI:

https://doi.org/10.54097/nt034d11

Keywords:

IRI Model, CFI Model, CSI Model, Building Value Index (BSI).

Abstract

Insurance companies have faced significant losses due to increasingly severe natural disasters, prompting a shift in their approach to extreme risks. To turn these risks into opportunities, it is essential to assess regional insurance risks. This study developed an Insurance Risk Index (IRI) Model using data from 20 regions based on six indicators, incorporating Analysis of Hierarchy Process (AHP), Entropy Weighting Method (EWM), and K-Means clustering. The model categorizes regions into three risk levels: Class I (low), Class II (medium), and Class III (high). Tokyo and California were selected for validation, with IRIs of 0.38 and 0.75, respectively, classifying Tokyo as low risk (Class I) and California as high risk (Class III). Additionally, the study combined the IRI with local economic and population data to assist communities and realtors in making informed decisions on building and development.

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References

[1] Zhang G, Li S, Wang X. An integrated assessment model for natural disaster risk in urban areas [J]. Journal of Environmental Management, 2019, 240: 472–480.

[2] Yang J, Xu G. Application of the analytic hierarchy process in comprehensive risk evaluation [J]. Risk Analysis, 2020, 40(6): 1079–1092.

[3] Li H, Chen Y. Entropy weighting method and its application in risk assessment [J]. Journal of Cleaner Production, 2017, 150: 328–336.

[4] Becker T, Gramlich P. Classification of urban disaster risk using clustering algorithms [J]. Natural Hazards, 2020, 98: 989–1005.

[5] Roberts M, Martinez D. The role of sensitivity analysis in assessing urban risk models [J]. Environmental Modelling & Software, 2020, 122: 103964.

[6] Brown L, Kim S. AHP in decision-making for insurance risk management [J]. Insurance Mathematics and Economics, 2019, 93: 70–78.

[7] Zhang Y, Smith R H, Zhou Q. Quantitative urban risk assessment for natural hazards using entropy weighting and AHP [J]. Natural Hazards Review, 2020, 21(3): 04020029.

[8] Wang S, Liu M. Classification techniques for spatial data analysis: A study on Fisher-Jenks algorithm [J]. Geospatial Information Science, 2020, 23(4): 309–318.

[9] Gupta K, Verma R. Impacts of natural disasters on insurance profitability: A global perspective [J]. Journal of Financial Services Research, 2019, 57: 77–92.

[10] White B, Lee T. Sustainable Urban Development through Construction Feasibility Index Modeling [M]. Mill Valley, CA: University Science, 2021.

[11] Miller A, Singh J. Multi-hazard risk assessment for urban areas [J]. Natural Hazards Review, 2021, 22(1): 1–12.