Coastal flood vulnerability evaluation method and system

Abstract

The invention relates to the technical field of marine disaster risk assessment, and discloses a coast flood vulnerability evaluation method and system, and the method comprises the steps: determining coast flood vulnerability evaluation indexes; dividing the target area into a plurality of grid units, extracting an actual value of each coast flood vulnerability evaluation index of each grid unit, and performing standardization processing; performing principal component analysis on each evaluation index after standardization processing, and determining the weight of each evaluation index; calculating the vulnerability index of each dimension of each grid unit according to the weight of each evaluation index and the standardized value of each evaluation index; obtaining a composite vulnerability index of each grid unit according to the vulnerability index of each dimension; and performing spatial analysis on the composite vulnerability index to generate a coastal zone vulnerability hot spot region map. According to the scheme, comprehensiveness, objectivity and spatial accuracy of coast flood vulnerability evaluation can be realized, and a scientific decision basis is provided for disaster risk prevention and control and spatial planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine disaster risk assessment, and particularly relates to a method and system for evaluating coastal flood vulnerability. Background Art

[0002] As a key area of land-sea interaction, the coastal zone faces serious threats from climate change and natural disasters. Frequent coastal flood events worldwide have caused huge impacts on the coastal social and economic systems. Scientifically evaluating the vulnerability of the coastal zone is the basis for formulating disaster prevention strategies. Existing studies mostly adopt single-dimensional indicators or static weight assignment models, which are difficult to comprehensively analyze the synergistic mechanism of natural and human elements, lack in-depth exploration of the laws of spatial heterogeneity, and restrict the accurate prevention and control of disaster risks and the effective implementation of spatial planning.

[0003] Traditional coastal vulnerability assessments mostly use equal-weight superposition or expert experience weight assignment methods, which have the problems of strong subjectivity in index weight assignment and difficulty in reflecting the internal relationship of data. Existing index systems mostly focus on a single dimension of nature or social economy, ignoring the coupling effect of multiple systems, resulting in one-sided evaluation results. Traditional models often use linear superposition or simple weighted average methods, which cannot effectively represent the non-linear interaction effects among exposure, sensitivity, and adaptability. Some methods do not distinguish the directionality of indicators, and the standardization process fails to eliminate the interference of dimensional differences on the evaluation results. Spatial analysis mostly relies on single vulnerability index classification mapping, lacking quantitative detection of spatial autocorrelation and aggregation laws, and it is difficult to identify the core areas of vulnerability propagation and diffusion. In addition, traditional evaluation models have insufficient ability to reduce the dimension of high-dimensional data, redundant indicators are likely to lead to distorted results, and lack a dynamic weight optimization mechanism, making it difficult to adapt to different regional characteristics. There are obvious technical bottlenecks in existing technologies in aspects such as massive multi-source data integration, complex system vulnerability analysis, and spatial heterogeneity identification.

[0004] Therefore, there is an urgent need to develop a method and system for evaluating coastal flood vulnerability, which can achieve comprehensiveness, objectivity, and spatial accuracy in coastal flood vulnerability evaluation, and provide a scientific decision-making basis for disaster risk prevention and control and spatial planning. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method and system for evaluating coastal flood vulnerability, which can achieve comprehensiveness, objectivity, and spatial accuracy in coastal flood vulnerability evaluation, and provide a scientific decision-making basis for disaster risk prevention and control and spatial planning.

[0006] The present invention provides a method for evaluating coastal flood vulnerability, and the method includes the following steps: S1. Determine the coastal flood vulnerability assessment indicators, and construct a four-dimensional indicator system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension, where each dimension includes exposure indicators, sensitivity indicators and adaptive capacity indicators; S2. Obtain the coastal flood data, geographic information data and socio-economic data of the target area, divide the target area into several grid cells, extract the actual values of each coastal flood vulnerability assessment indicator for each grid cell, and standardize the actual values of each coastal flood vulnerability assessment indicator; S3. Conduct principal component analysis on the standardized assessment indicators, and determine the weight of each assessment indicator according to the obtained eigenvalues and factor loading matrix; S4. Calculate the vulnerability index of each dimension of each grid cell according to the weights of each assessment indicator and the standardized values of each assessment indicator; S5. Obtain the composite vulnerability index of each grid cell according to the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension; S6. Conduct spatial analysis on the composite vulnerability index to generate a map of coastal vulnerability hotspots.

[0007] Further, in S2, the standardization of the actual values of each coastal flood vulnerability assessment indicator includes: Classify each coastal flood vulnerability assessment indicator into positive indicators and reverse indicators; The calculation formula for standardizing positive indicators is as follows: ; The calculation formula for standardizing reverse indicators is as follows: ; where, x j represents the actual value of the jth coastal flood vulnerability assessment indicator, μ j represents the arithmetic mean of the jth coastal flood vulnerability assessment indicator, σ j represents the standard deviation of the jth coastal flood vulnerability assessment indicator, and x j,norm represents the value after standardizing the jth coastal flood vulnerability assessment indicator.

[0008] Further, in S3, the principal component analysis of the standardized assessment indicators and the determination of the weight of each assessment indicator according to the obtained eigenvalues and factor loading matrix include: S31. Construct a covariance matrix according to the standardized assessment indicators; S32. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, and retain the eigenvalues greater than 1 as the principal components. S33. Calculate the corresponding factor loading matrix based on the eigenvalues of the principal components and the corresponding eigenvectors. The calculation formula is as follows: ; where L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, λ k represents the eigenvalue of the k-th principal component, a jk represents the j-th element in the eigenvector corresponding to the k-th principal component. S34. Calculate the weight of each evaluation index based on the eigenvalues of the principal components and the factor loading matrix.

[0009] Further, in S34, calculate the weight of each evaluation index based on the eigenvalues of the principal components and the factor loading matrix. The calculation formula is as follows: ; ; where w j represents the weight of the j-th coastal flood vulnerability evaluation index, k represents the k-th principal component, m represents the total number of principal components, λ k represents the eigenvalue of the k-th principal component, Σλ represents the sum of the eigenvalues of all principal components, L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, Σw represents the sum of the weights of all coastal flood vulnerability evaluation indexes, and w j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index.

[0010] Further, in S4, calculate the vulnerability index of each dimension of each grid cell based on the weights of each evaluation index and the standardized values of each evaluation index. The calculation formula is as follows: ; ; ; ; where E d , S d , AC d respectively represent the exposure index E, sensitivity index S, and adaptation capacity index AC corresponding to dimension d, FVI d represents the vulnerability index of dimension d, and x Ed,j,normThe value after standardization of the j-th coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, x Sd,j,norm The value after standardization of the j-th coastal flood vulnerability assessment index included in the sensitivity index S corresponding to dimension d, x ACd,j,norm The value after standardization of the j-th coastal flood vulnerability assessment index included in the adaptation capacity index AC corresponding to dimension d, w Ed,j,norm The normalized weight of the j-th coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, w Sd,j,norm The normalized weight of the j-th coastal flood vulnerability assessment index included in the sensitivity index S corresponding to dimension d, w ACd,j,norm The normalized weight of the j-th coastal flood vulnerability assessment index included in the adaptation capacity index AC corresponding to dimension d.

[0011] Furthermore, in S5, according to the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension, the composite vulnerability index of each grid cell is obtained, and the calculation formula is as follows: ; Among them, FVI represents the composite vulnerability index, FVI1 represents the vulnerability index of the social vulnerability dimension, FVI2 represents the vulnerability index of the economic vulnerability dimension, FVI3 represents the vulnerability index of the physical vulnerability dimension, and FVI4 represents the vulnerability index of the environmental vulnerability dimension.

[0012] Furthermore, in S6, a spatial analysis of the composite vulnerability index is carried out to generate a map of coastal vulnerability hotspots, including: S61. Conduct a hotspot analysis based on the composite vulnerability index of each grid cell; The calculation formula is as follows: ; Among them, represents the Getis-Ord statistic of the i-th grid cell, i represents the i-th grid cell, p represents the p-th grid cell, P represents the total number of grid cells, w ip represents the spatial weight between the i-th grid cell and the p-th grid cell, FVI p represents the composite vulnerability index of the p-th grid cell, represents the average value of the composite vulnerability indices of all grid cells, s represents the standard deviation of the composite vulnerability indices of all grid cells; S62. Generate a map of coastal vulnerability hotspots based on the Getis-Ord statistic.

[0013] The present invention also provides a coastal flood vulnerability assessment system for implementing the above-mentioned coastal flood vulnerability assessment method. The system includes the following modules: An evaluation system construction module, which is used to determine the coastal flood vulnerability assessment indicators and construct a four-dimensional indicator system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension. Each dimension includes exposure indicators, sensitivity indicators and adaptation capacity indicators; A standardization processing module, connected to the evaluation system construction module, which is used to obtain the coastal flood data, geographical information data and socio-economic data of the target area, extract the actual values of each coastal flood vulnerability assessment indicator, and perform standardization processing on the actual values of each coastal flood vulnerability assessment indicator; A weight calculation module, connected to the standardization processing module, which is used to perform principal component analysis on the standardized evaluation indicators, and determine the weight of each evaluation indicator according to the obtained eigenvalue and factor loading matrix; A vulnerability index calculation module, connected to the standardization processing module and the weight calculation module, which is used to calculate the vulnerability index of each dimension according to the weights of each evaluation indicator and the standardized values of each evaluation indicator; and obtain the composite vulnerability index of the target area according to the vulnerability indexes of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension; A hotspot map generation module, connected to the vulnerability index calculation module, which is used to perform spatial analysis on the composite vulnerability index and generate a coastal zone vulnerability hotspot area map.

[0014] The embodiments of the present invention have the following technical effects: Based on the construction of a four-dimensional indicator system, the present invention integrates multi-dimensional elements of society, economy, physics and environment, breaks through the limitations of traditional single-dimensional evaluation, comprehensively considers the complex vulnerability characteristics of the coastal zone system, decomposes and extracts the main components of the data through the principal component analysis method, combines the factor loading matrix and the proportion of eigenvalues to dynamically allocate the indicator weights, effectively eliminates indicator redundancy and solves the subjectivity problem of traditional expert weight assignment, and realizes the objective quantification of evaluation weights; the geometric mean model integrates multi-dimensional vulnerability indexes, avoids the sensitivity of the arithmetic mean to extreme values, and accurately reflects the short board effect of system vulnerability; spatial hotspot analysis combines the geographical weight matrix, and identifies the vulnerability spatial aggregation law through local spatial autocorrelation statistics, revealing the composite hotspot areas with high exposure, high sensitivity and low adaptation capacity. It realizes the leap from single indicator superposition to multi-dimensional coupling analysis in vulnerability evaluation, the transformation from static weight assignment to data-driven weight assignment, and the upgrade from plane evaluation to spatial heterogeneity identification, providing multi-dimensional decision support for accurate disaster risk identification and spatial planning optimization. Description of the Drawings

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0016] Figure 1 is a flowchart of a coastal flood vulnerability assessment method provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a system for a coastal flood vulnerability assessment method provided by an embodiment of the present invention. Specific Embodiments

[0017] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope protected by the present invention.

[0018] An embodiment of the present invention provides a coastal flood vulnerability assessment method, Figure 1 is a flowchart of a coastal flood vulnerability assessment method provided by an embodiment of the present invention. Refer to Figure 1 , the method includes the following steps: S1. Determine the coastal flood vulnerability assessment indicators, and construct a four-dimensional indicator system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension, where each dimension includes exposure indicators, sensitivity indicators and adaptation ability indicators.

[0019] Exemplarily, for the dimension of social vulnerability, the exposure evaluation indicators mainly consider the number or proportion of rural low-income population, population density, population growth rate, and rural area; low-income populations usually lack flood control infrastructure and emergency resources, live in low-lying and waterlogging-prone areas, and are more vulnerable to property losses and health threats during floods; in high-density areas, people are concentrated, and the risks of casualties, evacuation difficulties, and rescue pressures caused by floods increase significantly; rapid population expansion may lead to disorderly construction, occupying natural flood discharge areas (such as wetlands and river channels), exacerbating the flood exposure risk; rural areas often lack drainage systems and flood control projects, and large areas of farmland and residential areas are vulnerable to flood inundation. The sensitivity evaluation indicators mainly consider infant mortality rate, maternal mortality rate, proportion of urban population, proportion of female population, proportion of rural population, proportion of the elderly over 60 years old, and proportion of children under 5 years old; infant mortality rate and maternal mortality rate reflect the vulnerability of the medical system. When floods cause medical interruptions, the mother and baby groups face higher health risks due to their physiological vulnerability; urban areas rely on underground pipe networks and hardened surfaces, and it is difficult for waterlogging to subside quickly, and the risk of infrastructure failures is high; the proportion of the elderly and children has weak mobility and relies on external assistance, and power outages and water cut-offs caused by floods will directly threaten their survival. The adaptation ability evaluation indicators mainly consider shelters (including quantity, capacity, spatial accessibility, etc.), hospitals (quantity, capacity, spatial accessibility, etc.), and communication conditions; sufficient shelter space and convenient evacuation routes can reduce the risk of people staying behind and improve the survival rate during disasters; a high coverage rate of medical facilities can ensure the timely treatment of the injured and prevent secondary public health events; a stable communication network supports pre-disaster early warning, in-disaster coordination, and post-disaster reconstruction information transmission.

[0020] For the dimension of economic vulnerability, the exposure evaluation indicators mainly consider the land ownership and net sown area; agricultural land is vulnerable to flood inundation, resulting in crop failures and soil fertility losses, directly impacting the economic foundation. The sensitivity evaluation indicators mainly consider the proportion of agricultural employment and the dependency ratio; in regions with a single economic structure (relying on agriculture), agricultural losses caused by floods will trigger a chain of economic collapses; families with a high dependency ratio have a large proportion of their income used for consumption, insufficient savings after disasters, and weak recovery capabilities. The adaptation ability evaluation indicators mainly consider the amount of chemical fertilizer used, total irrigation area, proportion of employed population, and literacy rate; modern agricultural technologies (such as precision irrigation) can reduce the damage of floods to crops and maintain production resilience; a high employment rate and education level promote economic diversification and enhance the labor and technology reserves for post-disaster reconstruction.

[0021] For the physical vulnerability dimension, the exposure assessment indicators mainly consider building damage and road damage; poor building quality or road design defects will amplify the destructive power of floods, leading to infrastructure failures and hindered rescue operations. The sensitivity assessment indicators mainly consider the frequency of flood occurrences; high-frequency floods weaken the disaster resistance resilience of the region, increase the psychological pressure of residents, and reduce the long-term coping ability. The adaptation ability assessment indicators mainly consider the number of medical institutions, the number of hospital beds, the proportion of villages with stable electricity, and the proportion of commercial banks; sufficient medical resources ensure the treatment of the wounded and prevent secondary disasters (such as wound infections); electricity maintains the operation of key facilities (such as water pumps), and financial services support the post-disaster capital flow and reconstruction.

[0022] For the environmental vulnerability dimension, the exposure assessment indicators mainly consider the proportion of urbanized area and average rainfall; hardened surfaces (such as concrete) reduce rainwater infiltration, exacerbating surface runoff and the risk of waterlogging; the probability and intensity of floods in high-rainfall areas increase significantly. The sensitivity assessment indicators mainly consider the urban growth rate and temperature; rapid urbanization encroaches on natural flood storage areas (such as wetlands and lakes), weakening the ecological regulation ability; rising temperatures exacerbate the frequency of extreme rainfall and storm surges, indirectly amplifying the flood disaster chain. The adaptation ability assessment indicators mainly consider the proportion of forest area, the proportion of nature reserve area, and the forest growth rate; forests and wetlands can absorb rainwater and slow down runoff, and mangroves can dissipate waves and protect the shore, reducing the impact of floods; ecological restoration enhances the function of natural barriers and improves the long-term environmental resilience of the region.

[0023] In the specific implementation process, appropriate adjustments can be made according to the availability of data.

[0024] S2. Obtain the coastal flood data, geographical information data, and socioeconomic data of the target area, divide the target area into several grid cells, extract the actual values of each coastal flood vulnerability assessment indicator for each grid cell, and standardize the actual values of each coastal flood vulnerability assessment indicator.

[0025] In some embodiments, standardizing the actual values of each coastal flood vulnerability assessment indicator includes: Classify each coastal flood vulnerability assessment indicator into positive indicators and reverse indicators; in this solution, positive indicators represent assessment indicators where the larger the value, the higher the degree of coastal flood vulnerability, such as flood occurrence frequency, population density, etc., and the increase in their values directly reflects the enhancement of system vulnerability; reverse indicators represent assessment indicators where the larger the value, the lower the degree of vulnerability, such as the medical resource coverage rate related to adaptation ability, the proportion of forest area, etc., and the increase in their values reflects the optimization of the system's disaster resistance ability.

[0026] The calculation formula for standardizing positive indicators is as follows: ; The calculation formula for standardizing reverse indicators is as follows: ; where x j represents the actual value of the j-th coastal flood vulnerability evaluation indicator, μ j represents the arithmetic mean of the j-th coastal flood vulnerability evaluation indicator, σ j represents the standard deviation of the j-th coastal flood vulnerability evaluation indicator, and x j,norm represents the value after standardizing the j-th coastal flood vulnerability evaluation indicator.

[0027] The processed standardized data not only meets the requirements of principal component analysis for data distribution but also provides a unified benchmark for the comprehensive integration of multiple indicators. By distinguishing the directionality of indicators and adopting a differential standardization method, it can accurately reflect the positive or negative impact of each indicator on the vulnerability degree, ensuring the scientificity and rationality of subsequent weight calculation and index synthesis.

[0028] S3. Conduct principal component analysis on each standardized evaluation indicator, and determine the weight of each evaluation indicator according to the obtained eigenvalues and factor loading matrix.

[0029] In some embodiments, S3 includes the following sub-steps: S31. Construct a covariance matrix based on each standardized evaluation indicator.

[0030] Constructing a covariance matrix reflects the co-variation characteristics between indicators. The diagonal elements of the matrix represent the variance magnitudes of each indicator, and the off-diagonal elements reflect the co-variation relationship between indicators.

[0031] S32. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, and retain the eigenvalues greater than 1 as the principal components.

[0032] Eigenvalue decomposition transforms the covariance matrix into an eigenvector space. Each eigenvector corresponds to the main direction of data variation, and the eigenvalue magnitude represents the contribution degree of each principal component to explaining the original data variation.

[0033] S33. Calculate the corresponding factor loading matrix based on the eigenvalues of the principal components and the corresponding eigenvectors.

[0034] Principal component analysis extracts the main information through covariance matrix decomposition, retains the principal components with eigenvalues greater than one to simplify the data structure. The factor loading matrix reflects the correlation between the original indicators and the principal components. Combining eigenvalue calculation for weight can avoid the subjectivity of artificial weight assignment. The factor loading matrix is calculated by multiplying the eigenvector by the square root of the eigenvalue, revealing the projection intensity of each original indicator in the principal component space. The larger the absolute value of the loading, the stronger the explanatory ability of the indicator for the principal component.

[0035] Specifically, the calculation formula is as follows: ; Among them, L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, λ k represents the eigenvalue of the k-th principal component, a jk represents the j-th element in the eigenvector corresponding to the k-th principal component. The factor loading matrix is calculated by the product of the eigenvector and the square root of the eigenvalue, revealing the projection intensity of each original index in the principal component space. The larger the absolute value of the loading, the stronger the explanatory ability of the index for the principal component.

[0036] S34. Calculate the weight of each evaluation index according to the eigenvalue of the principal component and the factor loading matrix.

[0037] Specifically, the calculation formula is as follows: ; ; Among them, w j represents the weight of the j-th coastal flood vulnerability evaluation index, k represents the k-th principal component, m represents the total number of principal components, λ k represents the eigenvalue of the k-th principal component, Σλ represents the sum of the eigenvalues of all principal components, L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, Σw represents the sum of the weights of all coastal flood vulnerability evaluation indexes, w j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index.

[0038] The weight calculation comprehensively considers the variance contribution rate of the principal component and the factor loading intensity, and condenses multi-dimensional information into a comprehensive weight value through weighted summation. This method not only retains the advantage of dimension reduction and redundancy removal of principal component analysis, but also reflects the contribution differences of each index to the overall evaluation through the loading matrix, making the weight allocation have a mathematical statistical basis. In the calculation process, the proportion of eigenvalues determines the weight allocation of each principal component, and the absolute value of the factor loading reflects the importance degree of specific indexes. The combination of the two ensures that the weight system not only reflects the overall data structure characteristics, but also captures the influence differences of specific indexes. Empowering through principal component analysis effectively solves the subjectivity problem of the traditional expert scoring method, and at the same time avoids the defect of equal weight processing ignoring the importance degree differences of indexes, making the vulnerability evaluation results more objective and scientific.

[0039] S4. Calculate the vulnerability index of each dimension of each grid cell according to the weight of each evaluation index and the standardized value of each evaluation index.

[0040] The calculation of the dimensional vulnerability index needs to integrate the interaction relationships of three types of sub - indices. The exposure index reflects the potential intensity of the system facing disaster threats, the sensitivity index characterizes the response degree of the system to external disturbances, and the adaptive capacity index reflects the system's self - regulation and recovery potential.

[0041] Specifically, the calculation formula is as follows: ; ; ; ; Among them, E d 、S d 、AC d respectively represent the exposure index E, the sensitivity index S, and the adaptive capacity index AC corresponding to dimension d. FVI d represents the vulnerability index of dimension d. x Ed,j,norm represents the value after standardization of the j - th coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d. x Sd,j,norm represents the value after standardization of the j - th coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d. x ACd,j,norm represents the value after standardization of the j - th coastal flood vulnerability evaluation index included in the adaptive capacity index AC corresponding to dimension d. w Ed,j,norm represents the normalized weight of the j - th coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d. w Sd,j,norm represents the normalized weight of the j - th coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d. w ACd,j,norm represents the normalized weight of the j - th coastal flood vulnerability evaluation index included in the adaptive capacity index AC corresponding to dimension d.

[0042] The calculation of the vulnerability index adopts the mathematical form of the product of exposure and sensitivity divided by adaptive capacity, reflecting the dynamic balance relationship that vulnerability increases with the increase of the former two and decreases with the improvement of the latter. The calculation formula of this scheme is more in line with the action mechanism of multi - factor non - linear coupling in the actual disaster scenario. When the adaptive capacity is insufficient, small changes in exposure and sensitivity may cause significant fluctuations in vulnerability. By calculating the vulnerability index by dimension, the differences in vulnerability contributions of different system elements can be identified, providing directional guidance for the formulation of targeted disaster prevention measures.

[0043] S5. Obtain the composite vulnerability index of each grid cell according to the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension.

[0044] Specifically, the calculation formula is as follows: ; Among them, FVI represents the composite vulnerability index, FVI1 represents the vulnerability index of the social vulnerability dimension, FVI2 represents the vulnerability index of the economic vulnerability dimension, FVI3 represents the vulnerability index of the physical vulnerability dimension, and FVI4 represents the vulnerability index of the environmental vulnerability dimension.

[0045] The construction of the composite vulnerability index requires balancing the spatial heterogeneity of the multi-dimensional evaluation results. The geometric mean method realizes the fusion of multi-dimensional data through the product root operation, and can reduce the excessive influence of extreme values on the overall evaluation result compared with the arithmetic mean. The multiplicative relationship of the four-dimensional indices requires that the overall index can be at a low level only when each dimension maintains a low vulnerability, and the high vulnerability of any dimension will significantly increase the composite index value. The design of this formula conforms to the short-board effect theory of the disaster system. The geometric mean processing makes the composite index have the same sensitivity to the changes of each dimension. When the index of a certain dimension changes by an order of magnitude, the influence intensity can be balanced through the root operation. While retaining the independent contributions of each dimension, this method emphasizes the overall relevance of system vulnerability and avoids the risk hidden dangers of other dimensions being covered up by a single dominant dimension.

[0046] S6. Conduct a spatial analysis of the composite vulnerability index to generate a map of the vulnerability hotspots in the coastal zone.

[0047] In some embodiments, S6 uses the Getis-Ord method to conduct a spatial analysis of the composite vulnerability index, including the following sub-steps: S61. Conduct a hotspot analysis based on the composite vulnerability index of each grid cell; The calculation formula is as follows: ; Among them, represents the Getis-Ord statistic of the i-th grid cell, i represents the i-th grid cell, p represents the p-th grid cell, P represents the total number of grid cells, w ip represents the spatial weight between the i-th grid cell and the p-th grid cell. Exemplarily, it can be defined according to proximity. For example, when grid cell p is adjacent to grid cell i, then w ip is 1, otherwise w ip is 0; FVI p represents the composite vulnerability index of the p-th grid cell, represents the average value of the composite vulnerability indices of all grid cells, and s represents the standard deviation of the composite vulnerability indices of all grid cells.

[0048] S62. Generate a map of hotspots of coastal zone vulnerability based on the Getis-Ord statistic.

[0049] Among them, if is a positive value, it indicates that the area is a hotspot area with high vulnerability. If is a negative value, it indicates that the area is a cold spot area with low vulnerability.

[0050] The generated hotspot map can visually display the spatial differentiation characteristics of vulnerability through color gradients. Through spatial heterogeneity identification technology, it can provide a basis for formulating differential disaster prevention strategies, prioritize attention to vulnerable aggregation areas and analyze their formation mechanisms, thereby improving the spatial targeting and resource allocation efficiency of disaster management. The finally formed spatial distribution map not only reveals the current pattern, but also can analyze the vulnerability evolution trend through comparison with historical data, and establish a spatial benchmark framework for long-term monitoring and dynamic assessment.

[0051] Based on the construction of a four-dimensional index system, this invention integrates multi-dimensional elements of society, economy, physics, and environment, breaks through the limitations of traditional single-dimensional evaluation, comprehensively considers the complex vulnerability characteristics of the coastal zone system, decomposes and extracts the main components of data through the principal component analysis method, dynamically allocates index weights in combination with the factor loading matrix and the proportion of eigenvalues, effectively eliminates index redundancy and solves the subjectivity problem of traditional expert weight assignment, and realizes the objective quantification of evaluation weights; the geometric mean model integrates multi-dimensional vulnerability indices, avoids the sensitivity of arithmetic mean to extreme values, and accurately reflects the short-board effect of system vulnerability; spatial hotspot analysis combines the geographical weight matrix, and identifies the spatial aggregation law of vulnerability through local spatial autocorrelation statistics, revealing the composite hotspot areas with high exposure, high sensitivity, and low adaptability. It realizes the leap from single-index superposition to multi-dimensional coupling analysis in vulnerability evaluation, the transformation from static weight allocation to data-driven weight assignment, and the upgrade from plane evaluation to spatial heterogeneity identification, providing multi-dimensional decision support for accurate identification of disaster risks and optimization of spatial planning.

[0052] An embodiment of this invention also provides a coastal flood vulnerability evaluation system for implementing the above-mentioned coastal flood vulnerability evaluation method. Figure 2 It is a schematic diagram of the system structure of a coastal flood vulnerability evaluation method provided by an embodiment of this invention. Refer to Figure 2 , the system includes the following modules: An evaluation system construction module for determining coastal flood vulnerability evaluation indicators and constructing a four-dimensional index system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension, where each dimension includes exposure indicators, sensitivity indicators, and adaptation ability indicators. A standardization processing module, connected to the evaluation system construction module, is used to obtain coastal flood data, geographic information data, and socioeconomic data of the target area, extract the actual values of each coastal flood vulnerability evaluation index, and perform standardization processing on the actual values of each coastal flood vulnerability evaluation index; A weight calculation module, connected to the standardization processing module, is used to perform principal component analysis on each standardized evaluation index, and determine the weight of each evaluation index according to the obtained eigenvalue and factor loading matrix; A vulnerability index calculation module, connected to the standardization processing module and the weight calculation module, is used to calculate the vulnerability index of each dimension according to the weights of each evaluation index and the standardized values of each evaluation index; and obtain the composite vulnerability index of the target area according to the vulnerability indexes of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension; A hot spot map generation module, connected to the vulnerability index calculation module, is used to perform spatial analysis on the composite vulnerability index and generate a coastal zone vulnerability hot spot area map.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating coastal flood vulnerability, characterized in that The method includes the following steps: S1. Determine the coastal flood vulnerability assessment indicators, and construct a four-dimensional indicator system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension, where each dimension includes exposure indicators, sensitivity indicators, and adaptive capacity indicators; S2. Obtain the coastal flood data, geographic information data, and socioeconomic data of the target area, divide the target area into several grid cells, extract the actual values of each coastal flood vulnerability assessment indicator for each grid cell, and standardize the actual values of each coastal flood vulnerability assessment indicator; S3. Conduct principal component analysis on the standardized assessment indicators, and determine the weight of each assessment indicator according to the obtained eigenvalues and factor loading matrix; S4. Calculate the vulnerability index of each dimension of each grid cell according to the weights of each assessment indicator and the standardized values of each assessment indicator; S5. Obtain the composite vulnerability index of each grid cell according to the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension; S6. Conduct spatial analysis on the composite vulnerability index to generate a map of coastal vulnerability hotspots.

2. The coastal flood vulnerability assessment method according to claim 1, wherein In the S2, the standardization of the actual values of each coastal flood vulnerability assessment indicator includes: Classify each coastal flood vulnerability assessment indicator into positive indicators and reverse indicators; The calculation formula for standardizing positive indicators is as follows: ； The calculation formula for standardizing reverse indicators is as follows: ； Among them, x j represents the actual value of the j-th coastal flood vulnerability assessment index, μ j represents the arithmetic mean of the j-th coastal flood vulnerability assessment index, σ j represents the standard deviation of the j-th coastal flood vulnerability assessment index, x j,norm represents the value after standardization of the j-th coastal flood vulnerability assessment index.

3. The coastal flood vulnerability assessment method according to claim 1, wherein, In the S3, the principal component analysis of the standardized assessment indicators and the determination of the weight of each assessment indicator according to the obtained eigenvalues and factor loading matrix include: S31. Construct a covariance matrix according to the standardized assessment indicators; S32. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, and retain the eigenvalues greater than 1 as the principal components; S33. Calculate the corresponding factor loading matrix according to the eigenvalues of the principal components and the corresponding eigenvectors; The calculation formula is as follows: ； Among them, L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, λ k represents the eigenvalue of the k-th principal component, a jk represents the j-th element in the eigenvector corresponding to the k-th principal component; S34. Calculate the weight of each assessment indicator according to the eigenvalues of the principal components and the factor loading matrix.

4. The coastal flood vulnerability assessment method according to claim 3, characterized in that In the S34, the calculation of the weight of each assessment indicator according to the eigenvalues of the principal components and the factor loading matrix, the calculation formula is as follows: ； ； Among them, w j represents the weight of the j-th coastal flood vulnerability evaluation index, k represents the k-th principal component, m represents the total number of principal components, and λ k represents the eigenvalue of the k-th principal component, Σλ represents the sum of the eigenvalues of all principal components, and L jk represents the loading of the j-th coastal flood vulnerability evaluation index on the k-th principal component, Σw represents the sum of the weights of all coastal flood vulnerability evaluation indexes, and w j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index.

5. The coastal flood vulnerability assessment method according to claim 1, wherein, In the S4, the calculation of the vulnerability index of each dimension of each grid cell according to the weights of each assessment indicator and the standardized values of each assessment indicator, the calculation formula is as follows: ； ； ； ； Among them, E d , S d , AC d respectively represent the exposure index E, sensitivity index S, and adaptability index AC corresponding to dimension d. FVI d represents the vulnerability index of dimension d. x Ed,j,norm represents the value after standardization of the j-th coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d. x Sd,j,norm represents the value after standardization of the j-th coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d. x ACd,j,norm represents the value after standardization of the j-th coastal flood vulnerability evaluation index included in the adaptability index AC corresponding to dimension d. w Ed,j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d. w Sd,j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d. w ACd,j,norm represents the normalized weight of the j-th coastal flood vulnerability evaluation index included in the adaptability index AC corresponding to dimension d.

6. The coastal flood vulnerability assessment method according to claim 5, wherein, In the S5, the calculation of the composite vulnerability index of each grid cell according to the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension, the calculation formula is as follows: ； Among them, FVI represents the composite vulnerability index, FVI1 represents the vulnerability index of the social vulnerability dimension, FVI2 represents the vulnerability index of the economic vulnerability dimension, FVI3 represents the vulnerability index of the physical vulnerability dimension, and FVI4 represents the vulnerability index of the environmental vulnerability dimension.

7. The coastal flood vulnerability assessment method according to claim 1, wherein In the S6, the spatial analysis of the composite vulnerability index to generate a map of coastal vulnerability hotspots includes: S61. Conduct hot spot analysis based on the composite vulnerability index of each grid cell; The calculation formula is as follows: ； Among them, represents the Getis-Ord statistic of the i-th grid cell, where i represents the i-th grid cell, p represents the p-th grid cell, P represents the total number of grid cells, and w represents the spatial weight between the i-th grid cell and the p-th grid cell, and FVI ip represents the composite vulnerability index of the p-th grid cell, p and represents the average value of the composite vulnerability indices of all grid cells, and s represents the standard deviation of the composite vulnerability indices of all grid cells; S62. Generate a coastal vulnerability hot spot area map based on the Getis-Ord statistic.

8. A coastal flood vulnerability assessment system for implementing the coastal flood vulnerability assessment method according to any one of claims 1-7 above, characterized in that The system includes the following modules: An evaluation system construction module, which is used to determine the coastal flood vulnerability evaluation indicators and construct a four-dimensional index system including social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension, where each dimension includes exposure indicators, sensitivity indicators and adaptation capacity indicators; A standardization processing module, connected to the evaluation system construction module, which is used to obtain coastal flood data, geographic information data and socio-economic data of the target area, extract the actual values of each coastal flood vulnerability evaluation indicator, and perform standardization processing on the actual values of each coastal flood vulnerability evaluation indicator; A weight calculation module, connected to the standardization processing module, which is used to perform principal component analysis on each standardized evaluation indicator and determine the weight of each evaluation indicator according to the obtained eigenvalue and factor loading matrix; A vulnerability index calculation module, connected to the standardization processing module and the weight calculation module, which is used to calculate the vulnerability index of each dimension according to the weights of each evaluation indicator and the standardized values of each evaluation indicator; and obtain the composite vulnerability index of the target area according to the vulnerability indexes of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension; A hot spot map generation module, connected to the vulnerability index calculation module, which is used to perform spatial analysis on the composite vulnerability index and generate a coastal zone vulnerability hot spot area map.

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