Dynamic Risk Assessment of Compound Hazards Based on VFS-IEM-IDM: A Case Study of Typhoon-Rainstorm Hazards in Shenzhen, China
- 1Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
- 2Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- 1Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
- 2Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
Abstract. Typhoons and rainstorms are types of natural hazards that can cause significant impacts. These individual hazards may also occur simultaneously to produce compound hazards, leading to increased losses. The accurate risk assessment of such compound hazards faces several challenges due to the uncertainties in multiple hazards level evaluation, and the incomplete information in historical data sets. In this paper, to deal with these challenges, we propose a risk assessment model called VFS-IEM-IDM based on the Variable Fuzzy Set and Information Diffusion Method. In particular, VFS-IEM-IDM provides a comprehensive evaluation of the compound hazards level, and a predictive cumulative logistic model is used to verify the results. Furthermore, VFS-IEM-IDM applies a normal information diffusion estimator to estimate the conditional probability distribution and the vulnerability distribution of the compound hazards based on the hazards level, the hazards occurrence time, and the corresponding losses. To examine the efficacy of VFS-IEM-IDM, a case study of the Typhoon-Rainstorm hazards that occurred in Shenzhen, China is presented. The risk assessment results indicate that hazards of level Ⅱ mostly occur in August and October, while hazards of level Ⅲ often occur in September. The risk of the Typhoon-Rainstorm hazards differs in each month and in August and September the risk gets the highest value, and the estimated economic losses are around 114 million RMB and 167 million RMB respectively.
Wenwu Gong et al.
Status: final response (author comments only)
-
RC1: 'Comment on nhess-2021-263', Anonymous Referee #1, 09 Nov 2021
It is a complete and quality article.
The matrix in Line 237 must be reformatted (see attached file).
The grammar in the article needs to be improved. For example,
Line 30: There are many works discussing the multi-hazard risk assessment and Choi et al. (2021) had reviewed the relevant literature.
==> There are many works discussing the multi-hazard risk assessmentï¼ which have been reviewed by Choi et al. (2021).
- CC1: 'Reply on RC1', Gong Wenwu, 10 Nov 2021
-
AC1: 'Reply on RC1', Lili Yang, 08 Jan 2022
We are very grateful for your constructive suggestions for this manuscript, which is a great help and guidance for this study and our future research. Here are our responses to the comments and the details of how we made the changes in our manuscript.
-
RC2: 'Comment on nhess-2021-263', Anonymous Referee #2, 06 Dec 2021
In this paper, the authors propose a risk assessment model called VFS-IEM-IDM based on the variable fuzzy set and information diffusion method. And to examine the efficacy of VFS-IEM-IDM, a case study of typhoon rainstorm hazards that occurred in Shenzhen, China is presented. The method of the article is reasonable, but there are several problems, which need to be further improved. Therefore, a major revision is recommended.
Major Comments:
- Lines 74-81, if I understand it correctly based on the description, the main contribution of the paper is reflected in the first point, and the others are the improvement and verification of this method. Therefore, integrating this part with the last paragraph of introduction is recommended.
- At the end of the introduction, it is recommended to clearly point out the innovations of the paper and the main contributions of the authors. Technological innovation?
- Table 1. Classification standards of Typhoon-Rainstorm hazards. What is the reference for this standard? The precipitation in this table is the daily maximum precipitation or total precipitation? The strong wind here represents the maximum wind speed or extreme wind speed? Please define and explain them in detail.
- The monthly differences of different types of disasters may be closely related to the frequency of typhoons and the intensity of typhoons. What are the considerations in this paper?
- The data in the “Table A1”is the precipitation and strong wind data during the period affected by the typhoon rainstorms in Shenzhen, China. The source is the website of Shenzhen Meteorological Bureau. But there are big problems with the data in the table.
(1) According to the website guidelines, I checked the original data in 2017 and 2019, and found that the data given in the MP column in 2017 and 2018 are the maximum daily rainfall. However, the data given in the MP column in 2019 is the total rainfall. These two kinds of data are inconsistent.
(2) Similarly, I checked the wind data in 2017 and 2019, and found that SWI should represent extreme wind speeds. First, the English description needs to be revised. Second, compared with the original data, there are several wind speed errors. Please check carefully, because it is the basic data for this research.
(3) The Time column of this table may misleading the readers. It is recommended to give it according to the typhoon number, impact time, maximum daily rainfall / total rainfall, extreme wind speed, etc.
Minor comments:
- Line 101, please provide references as evidence.
- Line 194, please provide references or related websites.
- The limitations of this study and future plans are suggested to be addedin the conclusion.
- CC2: 'Reply on RC2', Gong Wenwu, 08 Dec 2021
-
AC2: 'Reply on RC2', Lili Yang, 08 Jan 2022
We are very grateful for your constructive suggestions for this manuscript, which is a great help and guidance for this study and our future research. Here are our responses to the comments and the details of how we made the changes in our manuscript.
Wenwu Gong et al.
Wenwu Gong et al.
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
397 | 113 | 17 | 527 | 9 | 10 |
- HTML: 397
- PDF: 113
- XML: 17
- Total: 527
- BibTeX: 9
- EndNote: 10
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1