Abstract
Abstract. Global warming has led to increased compound hazards, and an accurate risk assessment of such hazards is of great importance to urban emergency management. Due to the interrelations between multiple hazards, the risk assessment of a compound hazard faces several challenges: (1) the evaluation of hazard level needs to consider the correlations between compound hazard drivers, (2) usually only a small number of data samples are available for estimating the joint probability distribution of the compound hazard drivers and the loss caused by the hazards, and (3) the risk assessment process often ignores the temporal dynamics of compound hazard occurrences. This paper aims to address the mentioned challenges and develop an integrated risk assessment model VFS–IEM–IDM to quantify the dynamic risk of compound hazards based on variable fuzzy set theory (VFS), information entropy method (IEM), and information diffusion method (IDM). For the first challenge, VFS–IEM–IDM measures the effect of the compound hazard drivers via the use of relative membership degree and analyses the correlation between drivers with the entropy weight method, which is combined to evaluate compound hazard level. To address the second challenge, VFS–IEM–IDM applies the normal diffusion function to estimate the probability distribution of the compound hazard and the corresponding loss vulnerability curve. To deal with the third challenge, VFS–IEM–IDM assesses the risk of a compound hazard in different months based on the definition of probabilistic risk. In the end, this paper takes the typhoon–rainstorm disaster in Shenzhen, China, as an example to evaluate the effectiveness of the proposed VFS–IEM–IDM model. The results show that VFS–IEM–IDM effectively estimates the typhoon–rainstorm compound hazard level and assesses the dynamic risk of the compound hazards.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference31 articles.
1. Alan, A.: Generalized Odds Ratios for Ordinal Data, Biometrics, 36, 59–67, https://doi.org/10.2307/2530495, 1980. a
2. Beaula, T. and Partheeban, J.: Application of Variable Fuzzy Sets in the Analysis of Synthetic Disaster Degree for Flood Management, Int. J. Fuzzy Log. Syst., 5, 153–162, 2013. a
3. Chen, Y. and Yu, G.: Variable Fuzzy Sets and its Application in Comprehensive Risk Evaluation for Flood-control Engineering System, Fuzzy Optimiz. Decis. Mak., 5, 153–162, https://doi.org/10.1007/s10700-006-7333-y, 2006. a, b, c, d
4. Choi, E., Ha, J. G., and Min, K. K.: A review of multihazard risk assessment: Progress, potential, and challenges in the application to nuclear power plants, Int. J. Disast. Risk Reduct., 53, 19–33, https://doi.org/10.1016/j.ijdrr.2020.101933, 2021. a
5. Fang, Y., Zheng, X., Peng, H., Wang, H., and Xin, J.: A New Method of the Relative Membership Degree Calculation in Variable Fuzzy Sets for Water Quality Assessment, Ecol. Indic., 98, 515–522, https://doi.org/10.1016/j.ecolind.2018.11.032, 2019. a
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献