CN116596410A - Typhoon storm surge disaster vulnerability evaluation method based on combined weighting method - Google Patents
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Abstract
The application relates to the technical field of natural disaster evaluation, and particularly discloses a typhoon storm surge disaster vulnerability evaluation method based on a combined weighting method, which comprises the following steps: constructing an evaluation index of natural vulnerability and social vulnerability of a research area; acquiring an evaluation index of the regional vulnerability based on the evaluation index of the natural vulnerability and the social vulnerability; adopting a combined weighting method to carry out final weight calculation on evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability; and obtaining typhoon storm surge disaster vulnerability grades of the research areas based on the evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability and final weights. The application comprehensively considers the danger of disaster-causing environment and the vulnerability of disaster-bearing body, establishes a vulnerability evaluation index system of typhoon storm surge disaster, completes coastal typhoon storm surge disaster vulnerability evaluation work based on a combined weighting method from the aspects of nature, society and area, and ensures the comprehensiveness of index factors and the objectivity of an evaluation method.
Description
Technical Field
The application belongs to the technical field of natural disaster evaluation, and particularly relates to a typhoon storm surge disaster vulnerability evaluation method based on a combined weighting method.
Background
Storm surge is a phenomenon of local sea surface oscillation or aperiodic abnormal rise (fall) caused by strong wind and sudden change of air pressure accompanied by storm passing of tropical cyclone, temperate weather system, offshore wind and the like, and is one of the serious ocean disasters frequently occurring worldwide. Induced weather system features are generally classified into typhoon storm surge and temperate zone whirlwind storm surge. Coastline tortuous areas are subject to typhoons, thereby causing a number of chain disasters such as strong winds, heavy rainfall, storm floods, etc.
In the background of global climate warming in the past few decades, glaciers continue to melt, coastal level changes generally have a fluctuating rise, with coastal level rise rates of 3.4 mm/year, higher than the global average level of the same period since 1980. In addition, with the rapid development of ocean economy, further expansion of coastal projects such as seagoing land construction and the like, and the ground settlement easily caused by factors such as numerous high-rise buildings, unsmooth surface drainage systems and the like, the risk of storm surge disasters is increasingly prominent, and the ocean disaster prevention and reduction situation is very serious. Therefore, the risk assessment work of the vulnerability of the typhoon storm surge area of the coastal zone is an important precondition for disaster prevention and reduction.
Although the current research results aiming at typhoon storm surge vulnerability analysis are more, when the research area range is larger, the construction of diversified indexes on a fine scale is difficult, and the evaluation method mainly adopts a analytic hierarchy process and has a certain subjectivity.
Disclosure of Invention
The application aims to provide a typhoon storm surge disaster vulnerability evaluation method based on a combined weighting method so as to solve the problems in the prior art.
In order to achieve the above purpose, the application provides a typhoon storm surge disaster vulnerability evaluation method based on a combined weighting method, which comprises the following steps:
constructing an evaluation index of natural vulnerability and social vulnerability of a research area;
acquiring an evaluation index of the regional vulnerability based on the evaluation index of the natural vulnerability and the social vulnerability;
adopting a combined weighting method to carry out final weight calculation on evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability;
and acquiring typhoon storm surge disaster vulnerability grades of the research area based on the evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability and the final weight.
Optionally, the process of constructing the assessment indicator of natural vulnerability includes:
typhoon path data of a research area are acquired, and based on the typhoon path data, a GIS buffer analysis calculation is adopted to acquire typhoon disaster indexes;
calculating the monthly average precipitation of the research area, and calculating the ratio of the coastline length to the administrative area;
acquiring land utilization types of a research area, and calculating the frailty degree of the land utilization types by adopting a weighted comprehensive scoring method based on the land utilization types and frailty degree indexes corresponding to the land utilization types;
and constructing an evaluation index of natural vulnerability based on the typhoon disaster index, the average monthly precipitation, the ratio of coastline length to administrative area and the vulnerability of land utilization types.
Optionally, the process of constructing the evaluation index of social vulnerability includes:
constructing socioeconomic sensitivity indexes based on population density and GDP (GDP per unit area) of a research area;
constructing an adaptive assessment index based on labor population and financial income of the research area;
and constructing an evaluation index of social vulnerability based on the socioeconomic sensitivity index and the adaptability evaluation index.
Optionally, the process of performing final weight calculation on the evaluation indexes of the natural vulnerability, the social vulnerability and the regional vulnerability by adopting the combined weighting method comprises the following steps:
weighting the evaluation index by adopting a plurality of weighting methods respectively to acquire initial weights;
performing consistency check on the initial weights by adopting a Kendall consistency coefficient check method, and judging whether a plurality of weighting methods of the initial weights have consistency or not;
and selecting a weighting mode based on the consistency test result to carry out combined weighting on the evaluation index, and obtaining the final weight.
Optionally, the process of performing consistency check on the initial weight by adopting a Kendall consistency coefficient check method comprises the following steps:
converting the initial weights into a ranking matrix;
making an assumption as to whether the several weighting methods of the initial weight have consistency;
and calculating test statistics under different number of evaluation indexes, and carrying out consistency test on the hypothesis based on the test statistics.
Optionally, the process of selecting the weighting mode to perform combined weighting on the evaluation index based on the result of the consistency test includes:
when the weighting methods have consistency, calculating an arithmetic average value of the weighting methods, and acquiring a final weight based on the arithmetic average value;
when the weighting methods do not have consistency, calculating the grade correlation coefficient among the weighting methods, acquiring the weighting method with the highest consistency based on the grade correlation coefficient, constructing a weight vector based on the weighting method with the highest consistency and the grade correlation coefficient of other weighting methods, and calculating the final weight based on the weight vector.
Optionally, the process of obtaining the typhoon storm surge disaster vulnerability grade of the research area based on the evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability and the final weight comprises the following steps:
normalizing the evaluation index, multiplying the evaluation index by the final weight, and respectively obtaining index vulnerability scores of natural vulnerability, social vulnerability and regional vulnerability;
calculating standard deviation and mean of the index vulnerability scores;
dividing typhoon storm surge disaster vulnerability grades of the research areas based on the standard deviation and the mean value.
The application has the technical effects that:
the application provides a new method for evaluating the vulnerability of coastal typhoon storm surge disasters based on a combined weighting method, which introduces an area vulnerability model while defining the vulnerability concept, comprehensively considers the danger of a disaster-causing environment and the vulnerability of a disaster-bearing body, establishes a vulnerability evaluation index system of typhoon storm surge disasters, completes the vulnerability evaluation work of coastal typhoon storm surge disasters based on the combined weighting method from the aspects of nature, society and area, analyzes the distribution pattern and possible formation reasons of vulnerability levels, ensures the comprehensiveness of index factors and objectivity of the evaluation method on a wide range of research scales, and provides guidance and suggestion for disaster prevention and reduction.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a conceptual diagram of a regional disaster model according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a typhoon storm surge vulnerability evaluation method in an embodiment of the application;
fig. 3 is a schematic flow chart of a combined weighting method in an embodiment of the application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Embodiment one.
As shown in fig. 1-3, in this embodiment, a typhoon storm surge disaster vulnerability evaluation method based on a combined weighting method is provided, including:
the typhoon storm surge vulnerability evaluation method based on the HOP theory comprises the following steps:
the HOP theory is based on the research and study of Hewitt and Burton on regional ecology, and then a concept of regional disasters (Hazards of place) is put forward by a Cutter and the like on the basis, and a conceptual model of regional vulnerability is put forward. The basic idea of the model is: the regional risk of natural disasters is closely related to disaster reduction capacity and measures of a local social system, and potential disasters are generated by the combined action of the regional risk and measures. Potential disasters act on the level of geographical exposure to represent natural vulnerability and on the level of social systems to represent social vulnerability. The interaction of the two forms the regional comprehensive vulnerability. The regional vulnerability assessment result can reflect the vulnerability degree of a certain natural disaster locally, and can guide disaster reduction measures so as to reduce the potential hazard level.
The HOP model not only considers the geographical location characteristics of disasters and the physical attribute characteristics such as the types, the frequencies and the intensity of the disasters, but also emphasizes the vulnerability difference of the human social groups in different geographical locations in the aspect of social and economic characteristics; vulnerability studies are being developed towards coupled systems, focusing more on the evolution in time and space. Not only avoids the defect that a risk-disaster (R H) model only emphasizes the exposure degree and sensitivity of a disaster-bearing factor relative to disaster factors or environmental impact, but also makes up the defect that a pressure-release (pressure and release, RAP) model ignores the self analysis of a natural environment system and the interaction influence between the natural environment system and a human social system.
Therefore, based on the concept of HOP theory, the present patent starts from two aspects of geographical environment and social structure to evaluate the vulnerability level of the region, and considers the disaster causing condition of typhoon storm surge disaster and the availability of socioeconomic data, selects corresponding index factors to evaluate the natural vulnerability, social vulnerability and typhoon storm surge region vulnerability based on the two aspects, as shown in fig. 1.
The typhoon storm surge vulnerability evaluation mainly comprises four steps of index selection, weight determination, vulnerability score calculation and grade evaluation. Wherein, the weight is determined by a combined weighting method. The method is mainly characterized in that the principal and objective weighting methods have advantages and disadvantages, and the single weighting method is easy to bias due to the characteristics of the methods, so that the patent selects the combined weighting method to determine the weight in order to fully play the advantages of different weighting methods.
The specific flow is that firstly, a proper index factor is selected to construct a vulnerability index system, then, each index factor is subjected to forward or reverse sequencing, then, a weight coefficient is determined through combination weighting, the vulnerability scores of the three aspects of nature, society and area are calculated on the basis, and finally, the vulnerability result is divided into five evaluation grades according to the three aspects of nature, society and area through a standard deviation method, and the specific technical flow is shown in figure 2.
And (3) index selection:
according to the scientific, systematic, hierarchical, independent and other principles of index system construction and index selection, the assessment indexes of natural vulnerability and social vulnerability are respectively determined. The indexes for representing the natural vulnerability can truly and effectively reflect the disaster-pregnant environment of the region, and the accuracy and difficulty of acquiring the indexes are considered. Therefore, in view of the actual situation that a series of chain disasters such as strong wind, heavy rainfall, storm surge and the like are often caused when typhoons pass through the border, the natural vulnerability is measured by selecting typhoons disaster index, the average monthly precipitation of tropical cyclone high-incidence months, the ratio of coastline length to administrative area and the vulnerability of land utilization types.
Wherein the month average precipitation amount is the month precipitation amount average of 5 to 10 months of each county level unit of 2005-2021. The typhoon disaster index is obtained by carrying out GIS buffer analysis and calculation on typhoon path data, the radius of a buffer area in 2005-2016 is the average value of typhoon dimensions inverted by satellites, and the radius of the buffer area in 2017-2021 is determined to be 150 km (the influence scale of typhoons is generally 100-200 km). The buffer area in the single typhoon process is recorded as 1 time if the buffer area is overlapped with the research area, the non-overlapped area is recorded as 0 time, so that the disaster index influenced by tropical cyclone in 16 years in 236 coastal counties is calculated, and the calculation formula is as follows:
（1）
in the method, in the process of the application,disaster index indicating the jth county level city,/->Refers to the disaster frequency of the ith year of the jth county city.
The vulnerability of the land utilization type is determined according to the vulnerability value of the land utilization type corresponding to the storm surge disaster in the World Cover data. The World Cover data classifies land into 11 categories of woodland, shrubs, grasslands, cultivated lands, buildings, and the like. According to a storm surge disaster vulnerability grade table corresponding to the primary land use type, the vulnerability value of the land use type index is calculated, and the area percentage of the current primary land use type of the first three positions of occupied area is used as weight due to the large range of a research area, and the vulnerability value is calculated by using a weighted comprehensive scoring method, wherein the calculation formula is as follows.
（2）
In the method, in the process of the application,friable value of land use type referring to the ith county level city, +.>Refers to the area percentage of the type of land of the i-th county city, j, +.>Refers to the vulnerability index corresponding to the land type of the i-th county level city j, n=1, 2,3.
Social vulnerability is considered to be influenced by storm surge disasters of the socioeconomic disaster-bearing bodies and also considered to be self-reaction capacity and recovery capacity. Based on the sustainable development research thought, the patent defines the social vulnerability as the comprehensive level of sensitivity and adaptability, thereby establishing a corresponding social vulnerability index system. Among them, population number and GDP sum are important indexes of socioeconomic performance, and population density and GDP level per unit area can reflect vulnerability of social system, so it is used as socioeconomic performance index. When disaster resistance in the area is enhanced, the adaptability of the system to disasters is correspondingly improved. The indexes reflecting the disaster resistance mainly comprise labor population, financial income, disaster prevention and relief investment, sea defense level, waterlogging level and the like, and in view of data uniformity and availability, the labor population and the financial income are selected as adaptability evaluation indexes, as shown in a table 1 (coastal county vulnerability comprehensive measure index system), in the table 1, the larger the index type is, the higher the vulnerability is; the reverse direction indicates that the greater the index value, the lower the vulnerability thereof.
TABLE 1
Judging weights by a combined weighting method:
the weight of the index in the index system method is an important factor affecting the evaluation result, and the determination methods are numerous and can be roughly classified into two types: the first category is subjective weighting methods for performing research and judgment through expert experience, such as analytic hierarchy process, DEMATEL method and the like; the second category is an objective weighting method for performing index weight judgment based on the characteristics and the internal relation of the data, such as an entropy method, a CRITIC method and the like. The patent adopts a combined weighting method to judge the weight, and the general thought is to judge the initial vulnerability weight of each index, wherein the weight in the initial stage is calculated by each subjective and objective evaluation method respectively; and then, carrying out the prior inspection of the combined weighting by using a Kendall consistency coefficient inspection method, and selecting different combined weighting modes according to consistency inspection results to calculate the final weight.
The entropy weight method comprises the following steps: according to the variation degree of each index, calculating the entropy weight of each index by utilizing the information entropy, and correcting the weight of each index through the entropy weight. The CRITIC method includes: the objective weight of the index is comprehensively measured based on the contrast strength of the evaluation index and the conflict between the indexes, the correlation between the indexes is considered while the variability of the index is considered, and the objective attribute of the data is fully utilized for scientific evaluation. The DEMATEL method includes: the experience and knowledge of the expert are fully utilized, a direct influence matrix between factors is established, and the direct influence factors and the indirect influence factors and the importance of the factors in the system are judged through matrix transformation. Analytical hierarchy process: by analyzing the factors and the interrelationships contained in the complex system, the problem or object system is decomposed into a plurality of layers, and then the analysis is gradually and deeply performed from the global to the local from the outside to the inside.
The specific process is as follows:
first, initial weights of the indexes are determined by a CRITIC method, an entropy weight method, a hierarchical analysis method and a DEMATEL method respectively, then, the combined weights are checked in advance by a Kendall consistency coefficient check method, and different combined weights are selected according to consistency check results to calculate final weights, as shown in fig. 3.
Pre-test of combined weighting:
the prior check is to judge whether the weighted results of different methods have consistency. The method comprises the following specific steps: and the first step, converting the results obtained by the weighting methods into a sequencing matrix. Assume that n indexes are weighted by m methods, and the weighted results are shown in table 2. The weights of table 2 are converted to ranking values, the ranking results are shown in table 3.
TABLE 2
TABLE 3 Table 3
Second, establish hypothesis H0: the m weighting methods do not have consistency; h1: the m weighting methods have consistency.
Third, test statistics are calculated and the hypothesis is tested. When n is less than or equal to 7, the test statistic is:
。
in the method, in the process of the application,is Kendall consistency coefficient, +.>. Given significant level->Obtaining critical value ++by looking up critical value table of Kendall consistency coefficient s>. If->Then H0 is accepted; otherwise, reject H0 and accept H1.
When n > 7, the test statistic is:
。
in the method, in the process of the application,，/>。/>subject to the degree of freedom n-1 +.>Distribution, given significant level->Check->Distribution table critical value->. If->Then H0 is accepted; otherwise, reject H0 and accept H1.
Combination weighting:
and adopting different combination weighting modes according to the consistency judgment result of the prior test. Namely: if the m weighting methods accord with the consistency test, the result of the weighting method is smaller, and the reasonable combined weight can be obtained by calculating the arithmetic average value of the m methodsThe calculation is simple and convenient.
。
If the m weighting methods do not pass the consistency test, the consistency of the results obtained by the weighting methods is lower. For this case, a method with the highest consistency with respect to all other methods needs to be selected first, then the method is taken as a reference method, the correlation coefficient between the other methods and the reference method is calculated, and the index weight coefficient of the other methods is adjusted according to the magnitude of the correlation coefficient, so that the combination weighting is completed.
(1) The Spearman rank correlation coefficient is calculated. The calculation formula is as follows:
。
in the method, in the process of the application,，/>，（i，k=1， 2， ...， m; j=1， 2， ...， n）。
the Spearman grade correlation coefficient between the ith method and the kth method can reflect the consistency degree of the standards represented by the two methods when the two methods are entitled, and the spearmint is represented by the spearmint>The larger indicates the higher the consistency of the standards held by the two methods.
(2) The method for determining the highest relative consistency. First, the Spearman rank correlation coefficient highest value needs to be determinedThe method comprises the steps of carrying out a first treatment on the surface of the And then comparing the Spearman grade correlation coefficient of the method u and the method v with that of other methods respectively, and selecting the maximum value, and assuming the maximum value as the method u. Method u is the method with highest relative consistency in m weighting methods, and the Spearman level correlation coefficients of the method u and other methods form a vector +.>。
(3) Will bePerforming normalization to obtain weight vector->。
In the method, in the process of the application,（i=1， 2， ...， m）。
(4) Calculating combining weights。
。
Combining a weighting experiment with result analysis:
first, the present patent uses CRITIC method, entropy weight method, analytic hierarchy process and DEMATEL method to make initial weight determination of each index factor for natural index system, social index system and comprehensive index system, and the results are shown in table 4 (initial weight determination table of each index factor for natural and social index system) and table 5 (initial weight determination table of each index factor for regional comprehensive index system).
TABLE 4 Table 4
TABLE 5
Then, a ranking matrix is determined from the index weights, test statistics are calculated and a determination is made as to whether it passes the consistency test. The natural index system test result is rejection H1, H0 is accepted, namely the weighting method does not have consistency, wherein the CRITIC method is the method with highest relative consistency in the 4 weighting methods, the vector formed by the correlation coefficients of the Spearman grades of the CRITIC method and other methods is normalized to obtain a weight vector, and then the subsequent weighting treatment is carried out to obtain the combined weight of the weight vector. The test results of the society and the comprehensive index system are both H0 rejection and H1 acceptance, namely the weighting method has consistency, and the arithmetic mean value is calculated to be the combination weight. The result of the combination weights is shown in Table 6 (the result of determination of the combination weights of the index factors of the natural, social and comprehensive index systems).
TABLE 6
Calculating a vulnerability score:
the index factors of three index systems of nature, society and area are normalized and multiplied by the final weights obtained based on the method provided by the patent to obtain index vulnerability scores of the three index systems respectively, standard deviation Std. Dev and mean mu of the index systems are calculated and classified into five vulnerability classes of very low, medium and high and very high, and the classification standards are shown in table 7.
TABLE 7
Analysis of results:
study area selection:
in view of the restrictions of statistical data acquisition, 236 counties in coastal 11 provinces were selected as the study area.
Data material:
typhoon data 2005-2016 uses satellites provided by the Meteorological office tropical cyclone data center to analyze tropical cyclone data (version 2.0), which contains 6 hour position, intensity and scale information for all tropical cyclones captured by satellites in the North Pacific ocean. Typhoons data from 2017-2021 were from tropical cyclone best path datasets. The socioeconomic data is in the provinces, cities, district-level statistics annual survey, seventh population census bulletin and portal information and bulletin of 2021.
Experimental results:
the natural vulnerability grade is distributed in regional whole, and gradually decreases from the south to the north. The results show that coastal areas are highly vulnerable. Whereas the bay area is the least vulnerable area, the analytical reasons may be that tropical cyclones tend to fade in strength during inland movement, and energy continues to dissipate; moreover, the tropical cyclone in the area has less login times and low self energy, so that related disaster phenomena such as heavy rainfall, strong wind, storm surge and the like are not easy to cause.
The distribution of social vulnerability levels is more sporadic than natural vulnerability. Most coastal counties with highest vulnerability are areas with long coastline, small administrative area and low economic development, and the total number of coastal counties is 12. The analysis reasons are mainly that the area is limited by a narrow district area and a social development level, the exposure level of the disaster-bearing body is high, and the adaptability and the recovery power for typhoon storm surge disasters are poor. Most coastal cities with low vulnerability levels have high labor population density and high disaster resistance and reduction capability.
The regional vulnerability integrates evaluation indexes of both nature and society, and gradually decreases from the south to the north. The results show that the region with highest vulnerability is often coastal counties with typhoon disasters frequently occurring, low economic development level and poor disaster resistance, and the region with small area has higher vulnerability level. The speculated reasons are that although the natural vulnerability is higher, compared with the county of small size, the city with high economic activity can absorb more young labor force, has a more perfect and rapid rescue system, and greatly improves the disaster-fighting and relief level, thereby leading to lower comprehensive vulnerability level of the region. And in areas with low natural vulnerability, typhoons are less likely to cause disasters, and the whole area has low vulnerability level.
The application provides a new method for evaluating vulnerability of typhoon and storm surge disasters based on a combined weighting method, which takes 236 counties of coasts as basic geographic units, establishes a vulnerability evaluation index system of typhoon and storm surge disasters, completes the vulnerability evaluation work of typhoon and storm surge disasters based on the combined weighting method from the aspects of nature, society and area, and analyzes the distribution pattern and possible formation reasons of vulnerability grades.
While defining the vulnerability concept, an area vulnerability model is introduced, the danger of a disaster-causing environment and the vulnerability of a disaster-bearing body are comprehensively considered, a combined weighting method is adopted, coastal province and city and county level administrative units are taken as evaluation basic units, typhoon storm surge disaster vulnerability is evaluated and researched from three aspects of nature, society and area, the comprehensiveness of index factors and the objectivity of an evaluation method are ensured on a wide range of research scales, and the evaluation result can provide guidance and suggestion for disaster prevention and reduction.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (7)
1. The typhoon storm surge disaster vulnerability evaluation method based on the combined weighting method is characterized by comprising the following steps of:
constructing an evaluation index of natural vulnerability and social vulnerability of a research area;
acquiring an evaluation index of the regional vulnerability based on the evaluation index of the natural vulnerability and the social vulnerability;
adopting a combined weighting method to carry out final weight calculation on evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability;
and acquiring typhoon storm surge disaster vulnerability grades of the research area based on the evaluation indexes of natural vulnerability, social vulnerability and regional vulnerability and the final weight.
2. The typhoon storm surge disaster vulnerability assessment method based on the combined weighting method according to claim 1, wherein the process of constructing the natural vulnerability assessment index comprises the following steps:
typhoon path data of a research area are acquired, and based on the typhoon path data, a GIS buffer analysis calculation is adopted to acquire typhoon disaster indexes;
calculating the monthly average precipitation of the research area, and calculating the ratio of the coastline length to the administrative area;
acquiring land utilization types of a research area, and calculating the frailty degree of the land utilization types by adopting a weighted comprehensive scoring method based on the land utilization types and frailty degree indexes corresponding to the land utilization types;
and constructing an evaluation index of natural vulnerability based on the typhoon disaster index, the average monthly precipitation, the ratio of coastline length to administrative area and the vulnerability of land utilization types.
3. The typhoon storm surge disaster vulnerability assessment method based on the combined weighting method according to claim 1, wherein the process of constructing the social vulnerability assessment index comprises the following steps:
constructing socioeconomic sensitivity indexes based on population density and GDP (GDP per unit area) of a research area;
constructing an adaptive assessment index based on labor population and financial income of the research area;
and constructing an evaluation index of social vulnerability based on the socioeconomic sensitivity index and the adaptability evaluation index.
4. The typhoon storm surge disaster vulnerability assessment method based on the combined weighting method according to claim 1, wherein the process of carrying out final weight calculation on the assessment indexes of natural vulnerability, social vulnerability and regional vulnerability by adopting the combined weighting method comprises the following steps:
weighting the evaluation index by adopting a plurality of weighting methods respectively to acquire initial weights;
performing consistency check on the initial weights by adopting a Kendall consistency coefficient check method, and judging whether a plurality of weighting methods of the initial weights have consistency or not;
and selecting a weighting mode based on the consistency test result to carry out combined weighting on the evaluation index, and obtaining the final weight.
5. The typhoon storm surge disaster vulnerability assessment method based on combined weighting method as set forth in claim 4, wherein said process of carrying out consistency check on said initial weights by Kendall consistency coefficient check method comprises:
converting the initial weights into a ranking matrix;
making an assumption as to whether the several weighting methods of the initial weight have consistency;
and calculating test statistics under different number of evaluation indexes, and carrying out consistency test on the hypothesis based on the test statistics.
6. The method for evaluating vulnerability of typhoon storm surge disaster based on combined weighting method as set forth in claim 4, wherein the process of selecting weighting means for combined weighting said evaluation index based on the result of consistency check comprises:
when the weighting methods have consistency, calculating an arithmetic average value of the weighting methods, and acquiring a final weight based on the arithmetic average value;
when the weighting methods do not have consistency, calculating the grade correlation coefficient among the weighting methods, acquiring the weighting method with the highest consistency based on the grade correlation coefficient, constructing a weight vector based on the weighting method with the highest consistency and the grade correlation coefficient of other weighting methods, and calculating the final weight based on the weight vector.
7. The typhoon storm surge disaster vulnerability assessment method based on the combined weighting method according to claim 1, wherein the process of obtaining typhoon storm surge disaster vulnerability level of the research area based on the assessment indexes of natural vulnerability, social vulnerability and regional vulnerability and the final weight comprises the following steps:
normalizing the evaluation index, multiplying the evaluation index by the final weight, and respectively obtaining index vulnerability scores of natural vulnerability, social vulnerability and regional vulnerability;
calculating standard deviation and mean of the index vulnerability scores;
dividing typhoon storm surge disaster vulnerability grades of the research areas based on the standard deviation and the mean value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102622651A (en) * | 2012-02-21 | 2012-08-01 | 天津市市政工程设计研究院 | Method for building and improving green ecological road |
| AU2020102177A4 (en) * | 2020-04-26 | 2020-10-15 | National Marine Environmental Monitoring Center | Method for Evaluating Ecological Vulnerability of Island |
| CN113947280A (en) * | 2021-09-09 | 2022-01-18 | 北京理工大学 | Combined evaluation method based on feedback adjustment weight |
| CN115577981A (en) * | 2022-11-22 | 2023-01-06 | 山东大学 | Screening method, system, equipment and storage medium for cadmium-resistant varieties of crops |
| CN116187752A (en) * | 2022-12-27 | 2023-05-30 | 福建省气候中心 | Refined risk assessment method in typhoon disaster process |
-
2023
- 2023-07-18 CN CN202310875574.2A patent/CN116596410A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102622651A (en) * | 2012-02-21 | 2012-08-01 | 天津市市政工程设计研究院 | Method for building and improving green ecological road |
| AU2020102177A4 (en) * | 2020-04-26 | 2020-10-15 | National Marine Environmental Monitoring Center | Method for Evaluating Ecological Vulnerability of Island |
| CN113947280A (en) * | 2021-09-09 | 2022-01-18 | 北京理工大学 | Combined evaluation method based on feedback adjustment weight |
| CN115577981A (en) * | 2022-11-22 | 2023-01-06 | 山东大学 | Screening method, system, equipment and storage medium for cadmium-resistant varieties of crops |
| CN116187752A (en) * | 2022-12-27 | 2023-05-30 | 福建省气候中心 | Refined risk assessment method in typhoon disaster process |
Non-Patent Citations (3)
| Title |
|---|
| 张义丰等: "《淮河环境与治理》", 测绘出版社, pages: 45 * |
| 曾宪报: "组合赋权法新探", 《预测》, no. 5, pages 69 * |
| 谭丽荣: "中国沿海地区风暴潮灾害综合脆弱性评估", 《中国博士学位论文全文数据库基础科学辑》, no. 11, pages 1 * |
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