Vulnerability mapping of typhoon-induced flooding based on ECC model

Zi-Kai Chen , Shui-Long Shen , Qian Zheng

Smart Construction and Sustainable Cities ›› 2026, Vol. 4 ›› Issue (1) : 9

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Smart Construction and Sustainable Cities ›› 2026, Vol. 4 ›› Issue (1) :9 DOI: 10.1007/s44268-026-00086-w
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Vulnerability mapping of typhoon-induced flooding based on ECC model
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Abstract

Typhoons lead to meteorological disasters along the coastal region of China. Typhoons induce secondary hazards in cities such as flooding, landslides, vibration of high-rise buildings etc. This paper introduces an improved multi-criteria decision-making (MCDM) method, called the ECC model, for assessing the distribution of vulnerability to typhoon-induced flooding. The novelty of this method is its integration of weights calculated initially using the traditional entropy weight method (EWM), coefficient of variation method (CVM), and criteria importance through intercriteria correlation (CRITIC) method through a matrix-based weight combination approach; the vulnerability level is reflected through comprehensive scores. By applying historical data related to typhoon-induced flood disasters to this method, the vulnerability distribution in the Hainan Island region was estimated. The collected statistical data demonstrated that the proposed improved assessment method achieved results that corresponded highly with actual disaster situations, indicating the reliability of this method. Moreover, the results showed that the proposed ECC model predicted the disaster site due to the Yagi event accurately and reflected the field situation. This study serves as a valuable reference for disaster prevention/mitigation measurement.

Keywords

Vulnerability mapping / Typhoon / EWM-CVM-CRITIC / ECC model

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Zi-Kai Chen, Shui-Long Shen, Qian Zheng. Vulnerability mapping of typhoon-induced flooding based on ECC model. Smart Construction and Sustainable Cities, 2026, 4(1): 9 DOI:10.1007/s44268-026-00086-w

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Funding

Basic and Applied Basic Research Foundation of Guangdong Province(2022A1515240073)

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