Coastal flood vulnerability assessment method and system

Abstract

The present application relates to the technical field of marine disaster risk assessment, and discloses a coastal flood vulnerability evaluation method and system, which comprises the following steps: determining the evaluation indexes of coastal flood vulnerability; dividing the target area into a plurality of grid units, extracting the actual values of the evaluation indexes of coastal flood vulnerability of each grid unit, and performing standardization processing; performing principal component analysis on each evaluation index after standardization processing to determine 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 value of each evaluation index after standardization; obtaining the 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 hotspot area map. The present application can realize the comprehensiveness, objectivity and spatial accuracy of coastal flood vulnerability evaluation, and provide a scientific decision basis 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 in particular relates to a coastal flood vulnerability assessment method and system. Background Art

[0002] Coastal zones, as key areas of land-sea interaction, face severe threats from climate change and natural disasters. Frequent coastal flooding events worldwide have a significant impact on coastal socioeconomic systems. Scientifically assessing coastal vulnerability is fundamental to developing disaster prevention strategies. Existing research often uses single-dimensional indicators or static weighting models, which struggle to comprehensively analyze the synergistic mechanisms between natural and human factors and lacks in-depth exploration of spatial heterogeneity, hindering the precise prevention and control of disaster risks and the effective implementation of spatial planning.

[0003] Traditional coastal vulnerability assessments often employ equal-weighted superposition or expert experience-based weighting methods, resulting in highly subjective indicator weight assignment and a failure to reflect inherent data correlations. Existing indicator systems often focus on a single natural or socioeconomic dimension, neglecting the effects of multi-system coupling, leading to one-sided assessment results. Traditional models often employ linear superposition or simple weighted averages, failing to effectively characterize the nonlinear interactions between exposure, sensitivity, and adaptive capacity. Some methods fail to distinguish between indicator directions, and standardization fails to eliminate the influence of dimensional differences on assessment results. Spatial analysis often relies on a single vulnerability index for grading and mapping, lacking quantitative detection of spatial autocorrelation and clustering patterns, making it difficult to identify core areas of vulnerability transmission and diffusion. Furthermore, traditional assessment models lack the ability to reduce high-dimensional data, distort results due to redundant indicators, and lack dynamic weight optimization mechanisms, making them difficult to adapt to diverse regional characteristics. Existing technologies face significant technical bottlenecks in integrating massive multi-source data, analyzing the vulnerability of complex systems, and identifying spatial heterogeneity.

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

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

[0006] The present invention provides a coastal flood vulnerability assessment method, which comprises the following steps:

[0007] S1, determine a coastal flood vulnerability evaluation index, and construct a four-dimensional index system including a social vulnerability dimension, an economic vulnerability dimension, a physical vulnerability dimension, and an environmental vulnerability dimension, wherein each dimension includes an exposure index, a sensitivity index, and an adaptive capacity index;

[0008] S2, obtain coastal flood data, geographic information data, and social and economic data of a target area, divide the target area into a plurality of grid cells, extract actual values of each coastal flood vulnerability evaluation index of each grid cell, and perform standardization processing on the actual values of each coastal flood vulnerability evaluation index;

[0009] S3, perform principal component analysis on each evaluation index after standardization processing, and determine the weight of each evaluation index according to the obtained characteristic value and factor loading matrix;

[0010] S4, calculate the vulnerability index of each dimension of each grid cell according to the weight of each evaluation index and the value of each evaluation index after standardization;

[0011] S5, obtain a composite vulnerability index of each grid cell according to the vulnerability indexes of the social vulnerability dimension, the economic vulnerability dimension, the physical vulnerability dimension, and the environmental vulnerability dimension;

[0012] S6, perform spatial analysis on the composite vulnerability index to generate a coastal zone vulnerability hotspot area map.

[0013] Further, in S2, the actual values of each coastal flood vulnerability evaluation index are standardized, including:

[0014] dividing each coastal flood vulnerability evaluation index into a positive index and a reverse index;

[0015] The calculation formula for standardizing the positive index is as follows:

[0016] ;

[0017] The calculation formula for standardizing the reverse index is as follows:

[0018] ;

[0019] wherein x j represents the actual value of the jth coastal flood vulnerability evaluation index, μ j represents the arithmetic mean of the jth coastal flood vulnerability evaluation index, σ j represents the standard deviation of the jth coastal flood vulnerability evaluation index, x j,norm represents the value of the jth coastal flood vulnerability evaluation index after standardization.

[0020] Furthermore, in S3, principal component analysis is performed on each evaluation index after standardization, and the weight of each evaluation index is determined based on the obtained eigenvalue and factor loading matrix, including:

[0021] S31, constructing a covariance matrix based on the standardized evaluation indicators;

[0022] S32, performing eigenvalue decomposition according to the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and retaining eigenvalues ​​greater than 1 as principal components;

[0023] S33, calculate the corresponding factor loading matrix based on the eigenvalues ​​of the principal components and the corresponding eigenvectors;

[0024] The calculation formula is as follows:

[0025] ;

[0026] Among them, L jk represents the load of the jth coastal flood vulnerability evaluation index on the kth principal component, λ k represents the eigenvalue of the kth principal component, a qk Represents the qth element in the eigenvector corresponding to the kth principal component;

[0027] S34. Calculate the weight of each evaluation indicator based on the eigenvalue of the principal component and the factor loading matrix.

[0028] Furthermore, in S34, the weight of each evaluation indicator is calculated based on the eigenvalue of the principal component and the factor loading matrix. The calculation formula is as follows:

[0029] ;

[0030] ;

[0031] Among them, w j represents the weight of the jth coastal flood vulnerability evaluation index, k represents the kth principal component, m represents the total number of principal components, λ k represents the eigenvalue of the kth principal component, Σλ represents the sum of the eigenvalues ​​of all principal components, and L jk represents the load of the jth coastal flood vulnerability evaluation index on the kth 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 assessment index.

[0032] Furthermore, in S4, the vulnerability index of each dimension of each grid unit is calculated based on the weight of each evaluation indicator and the standardized value of each evaluation indicator. The calculation formula is as follows:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] Among them, E d 、S d 、AC d They represent the exposure index E, sensitivity index S, adaptability index AC, and FVI corresponding to dimension d. d represents the vulnerability index of dimension d, x Ed,j,norm represents the standardized value of the jth coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d, x Sd,j,norm represents the standardized value of the jth coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d, x ACd,j,norm represents the standardized value of the jth 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 jth coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, w Sd,j,norm represents the normalized weight of the jth coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d, w ACd,j,norm It represents the normalized weight of the j-th coastal flood vulnerability evaluation index included in the adaptive capacity index AC corresponding to dimension d.

[0038] Furthermore, in S5, the composite vulnerability index of each grid unit is obtained based on the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension. The calculation formula is as follows:

[0039] ;

[0040] 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.

[0041] Furthermore, in S6, the composite vulnerability index is spatially analyzed to generate a coastal vulnerability hotspot map including:

[0042] S61. Conduct hotspot analysis based on the composite vulnerability index of each grid cell;

[0043] The calculation formula is as follows:

[0044] ;

[0045] in, represents the Getis-Ord of the i-th grid cell Statistics, 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 index of all grid cells, and s represents the standard deviation of the composite vulnerability index of all grid cells;

[0046] S62, according to Getis-Ord Statistics are used to generate a hotspot map of coastal vulnerability.

[0047] The present invention also provides a coastal flood vulnerability assessment system for executing the above coastal flood vulnerability assessment method. The system includes the following modules:

[0048] An evaluation system construction module is used to determine coastal flood vulnerability evaluation indicators and construct a four-dimensional indicator system consisting of social vulnerability, economic vulnerability, physical vulnerability, and environmental vulnerability dimensions, where each dimension includes exposure indicators, sensitivity indicators, and adaptive capacity indicators;

[0049] The standardization processing module is connected to the evaluation system construction module and 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 standardize the actual values ​​of each coastal flood vulnerability evaluation index;

[0050] The weight calculation module is connected to the standardization processing module and is used to perform principal component analysis on each evaluation index after standardization processing, and determine the weight of each evaluation index according to the obtained eigenvalue and factor load matrix;

[0051] 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 based on the weight of each evaluation indicator and the standardized value of each evaluation indicator; and to obtain a composite vulnerability index of the target area based on the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension;

[0052] The hotspot map generation module is connected to the vulnerability index calculation module and is used to perform spatial analysis on the composite vulnerability index and generate a coastal vulnerability hotspot area map.

[0053] The embodiments of the present invention have the following technical effects:

[0054] This paper, based on the construction of a four-dimensional indicator system, integrates social, economic, physical, and environmental multi-dimensional factors, breaking through the limitations of traditional single-dimensional evaluation and comprehensively considering the complex vulnerability characteristics of coastal systems. Principal component analysis is used to decompose and extract the principal components of the data. Indicator weights are dynamically assigned by combining the factor loading matrix and the eigenvalue ratio, effectively eliminating indicator redundancy and addressing the subjectivity of traditional expert weighting, thus achieving objective quantification of evaluation weights. The geometric mean model integrates multi-dimensional vulnerability indices, avoiding the arithmetic mean's sensitivity to extreme values ​​and accurately reflecting the weak-board effect of system vulnerability. Spatial hotspot analysis, combined with a geographic weight matrix, identifies the spatial clustering patterns of vulnerability through local spatial autocorrelation statistics, revealing complex hotspot areas characterized by high exposure, high sensitivity, and low adaptability. This approach achieves a transition from single-indicator superposition to multi-dimensional coupled analysis, from static weight assignment to data-driven weighting, and from planar evaluation to spatial heterogeneity identification, providing multi-dimensional decision support for the precise identification of disaster risks and the optimization of spatial planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of a coastal flood vulnerability assessment method provided by an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the system structure of a coastal flood vulnerability assessment method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0059] The embodiment of the present invention provides a coastal flood vulnerability assessment method. Figure 1 This is a flow chart of a coastal flood vulnerability assessment method provided by an embodiment of the present invention, see Figure 1 , the method comprises the following steps:

[0060] S1. Determine the coastal flood vulnerability evaluation 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 index, sensitivity index and adaptability index.

[0061] For example, for the dimension of social vulnerability, the exposure assessment indicators mainly consider the number or proportion of rural poor people, population density, population growth rate, and rural area; low-income people usually lack flood control infrastructure and emergency resources, live in low-lying and flood-prone areas, and are more vulnerable to property losses and health threats when floods occur; people are concentrated in high-density areas, and the risk of casualties, evacuation difficulties and rescue pressure caused by floods increase significantly; rapid population expansion may lead to disorderly construction, encroach on natural flood discharge areas (such as wetlands and rivers), and increase the risk of flood exposure; rural areas often lack drainage systems and flood control projects, and large areas of farmland and residential areas are susceptible to flooding. Sensitivity assessment indicators primarily consider infant mortality, maternal mortality, proportion of urban population, proportion of female population, proportion of rural population, proportion of people aged 60 and above, and proportion of children under 5. Infant and maternal mortality rates reflect the vulnerability of the healthcare system. When floods disrupt healthcare, mothers and infants face increased health risks due to their physiological vulnerabilities. Urban areas rely on underground pipelines and hardened surfaces, making it difficult for waterlogging to recede quickly and increasing the risk of infrastructure failure. The proportion of elderly and children is limited in mobility and relies on external assistance, making water and power outages caused by floods a direct threat to their survival. Adaptability assessment indicators primarily consider shelters (including number, capacity, and accessibility), hospitals (number, capacity, and accessibility), and communications. Adequate shelter space and convenient evacuation routes can reduce the risk of stranded people and improve survival rates during disasters. High coverage of medical facilities ensures timely treatment for the injured and prevents secondary public health incidents. A stable communications network supports pre-disaster warning, coordination during a disaster, and information transmission for post-disaster reconstruction.

[0062] Regarding economic vulnerability, exposure indicators primarily consider land ownership and net sown area. Agricultural land is susceptible to flooding, leading to crop failure and soil fertility loss, directly impacting the economic base. Sensitivity indicators primarily consider the proportion of people employed in agriculture and the dependency ratio. In regions with a single economic structure (dependent on agriculture), agricultural losses caused by floods can trigger cascading economic collapses. Households with high dependency ratios spend a large proportion of their income on consumption, resulting in insufficient post-disaster savings and weaker recovery capacity. Adaptability indicators primarily consider fertilizer use, total irrigated area, employment, and literacy rates. Modern agricultural technologies (such as precision irrigation) can reduce flood damage to crops and maintain production resilience. High employment rates and education levels promote economic diversification and enhance the labor and technical reserves for post-disaster reconstruction.

[0063] Regarding physical vulnerability, exposure assessment indicators primarily consider damage to houses and roads. Poor building quality or flawed road design can amplify flood damage, leading to infrastructure failures and hindering rescue efforts. Sensitivity assessment indicators primarily consider flood frequency. High flood frequency weakens regional resilience, increases psychological stress among residents, and reduces long-term coping capabilities. Adaptability assessment indicators primarily consider the number of medical institutions, the number of beds, the proportion of villages with stable electricity, and the proportion of commercial banks. Adequate medical resources ensure treatment for the injured and prevent secondary disasters (such as wound infections); electricity maintains the operation of critical facilities (such as water pumps); and financial services support post-disaster capital flows and reconstruction.

[0064] Regarding environmental vulnerability, exposure assessment indicators primarily consider the proportion of urbanized areas and average rainfall. Hardened surfaces (e.g., concrete) reduce rainwater infiltration, exacerbating surface runoff and waterlogging risks. The probability and intensity of floods in high-rainfall areas significantly increase. Sensitivity assessment indicators primarily consider urban growth rate and temperature. Rapid urbanization encroaches on natural flood storage areas (e.g., wetlands and lakes), weakening ecological regulation. Rising temperatures increase the frequency of extreme rainfall and storm surges, indirectly amplifying the flood disaster chain. Adaptability assessment indicators primarily consider the proportion of forested areas, the proportion of nature reserves, and forest growth rate. Forests and wetlands absorb rainwater and slow runoff, while mangroves provide wave and bank protection, mitigating flood impacts. Ecological restoration strengthens natural barriers and enhances regional environmental resilience over the long term.

[0065] During the specific implementation process, appropriate adjustments can be made based on the availability of data.

[0066] S2. Obtain coastal flood data, geographic information data, and socioeconomic data of the target area, divide the target area into several grid cells, extract the actual value of each coastal flood vulnerability evaluation index of each grid cell, and standardize the actual value of each coastal flood vulnerability evaluation index.

[0067] In some embodiments, normalizing the actual values ​​of each coastal flood vulnerability assessment index includes:

[0068] The coastal flood vulnerability evaluation indicators are divided into positive indicators and negative indicators. In this scheme, positive indicators refer to evaluation indicators whose larger values ​​indicate higher coastal flood vulnerability, such as flood frequency, population density, etc., and their numerical increase directly reflects the increase in system vulnerability. Negative indicators refer to evaluation indicators whose larger values ​​indicate lower vulnerability, such as medical resource coverage rate and forest area ratio related to adaptability, and their numerical increase reflects the optimization of the system's disaster resistance capacity.

[0069] The calculation formula for normalizing the positive indicators is as follows:

[0070] ;

[0071] The calculation formula for normalizing the reverse indicator is as follows:

[0072] ;

[0073] Among them, x j represents the actual value of the jth coastal flood vulnerability evaluation index, μ j represents the arithmetic mean of the j-th coastal flood vulnerability evaluation index, σ j represents the standard deviation of the j-th coastal flood vulnerability evaluation index, x j,norm Represents the standardized value of the j-th coastal flood vulnerability assessment index.

[0074] The processed, standardized data not only meets the data distribution requirements of principal component analysis but also provides a unified benchmark for the comprehensive integration of multiple indicators. By distinguishing the directionality of indicators and adopting differentiated standardization methods, it can accurately reflect the positive or negative impact of each indicator on the degree of vulnerability, ensuring the scientific and rationality of subsequent weight calculations and index synthesis.

[0075] S3. Perform principal component analysis on each evaluation indicator after standardization, and determine the weight of each evaluation indicator based on the obtained eigenvalue and factor loading matrix.

[0076] In some embodiments, S3 includes the following sub-steps:

[0077] S31. Construct a covariance matrix based on the standardized evaluation indicators.

[0078] The covariance matrix is ​​constructed to reflect the coordinated change characteristics among the indicators. The diagonal elements of the matrix represent the variance of each indicator, and the off-diagonal elements reflect the covariation relationship among the indicators.

[0079] S32. Perform eigenvalue decomposition according to the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and retain eigenvalues ​​greater than 1 as principal components.

[0080] Eigenvalue decomposition converts the covariance matrix into an eigenvector space. Each eigenvector corresponds to the main direction of data variation, and the size of the eigenvalue represents the contribution of each principal component to explaining the variation of the original data.

[0081] S33. Calculate the corresponding factor loading matrix based on the eigenvalues ​​of the principal components and the corresponding eigenvectors.

[0082] Principal component analysis extracts key information through covariance matrix decomposition, retaining 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. Calculating weights based on eigenvalues ​​avoids the subjectivity of artificial weighting. The factor loading matrix, calculated by multiplying the eigenvectors by the square roots of the eigenvalues, reveals the projection strength of each original indicator into the principal component space. Larger absolute values ​​of the loadings indicate a stronger explanatory power of the indicator for the principal component.

[0083] Specifically, the calculation formula is as follows:

[0084] ;

[0085] Among them, L jk represents the load of the jth coastal flood vulnerability evaluation index on the kth principal component, λ k represents the eigenvalue of the kth principal component, a qk The qth element in the eigenvector corresponding to the kth principal component is represented by the factor loading matrix, which is calculated by multiplying the eigenvector by the square root of the eigenvalue. This matrix reveals the projection strength of each original indicator in the principal component space. A larger absolute value of the loading indicates a stronger explanatory power of the indicator for the principal component.

[0086] S34. Calculate the weight of each evaluation indicator based on the eigenvalue of the principal component and the factor loading matrix.

[0087] Specifically, the calculation formula is as follows:

[0088] ;

[0089] ;

[0090] Among them, w j represents the weight of the jth coastal flood vulnerability evaluation index, k represents the kth principal component, m represents the total number of principal components, λ k represents the eigenvalue of the kth principal component, Σλ represents the sum of the eigenvalues ​​of all principal components, and L jkrepresents the load of the jth coastal flood vulnerability evaluation index on the kth 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 assessment index.

[0091] The weight calculation comprehensively considers the variance contribution of the principal components and the strength of the factor loadings, condensing multidimensional information into a comprehensive weight value through weighted summation. This method retains the advantages of principal component analysis in dimensionality reduction and redundancy removal, while also reflecting the differences in the contribution of each indicator to the overall evaluation through the loading matrix, giving the weight distribution a mathematical statistical basis. During the calculation process, the proportion of eigenvalues ​​determines the weight distribution of each principal component, and the absolute value of the factor loading reflects the importance of the specific indicator. The combination of these two ensures that the weight system reflects the overall data structure characteristics and captures the differences in the influence of specific indicators. The weighting through principal component analysis effectively addresses the subjectivity of traditional expert scoring methods, while avoiding the flaw of equal weight treatment that ignores differences in the importance of indicators, making the vulnerability assessment results more objective and scientific.

[0092] S4. Calculate the vulnerability index of each dimension of each grid unit according to the weight of each evaluation indicator and the standardized value of each evaluation indicator.

[0093] The calculation of the dimensional vulnerability index requires the integration of the interaction between the three types of sub-indices. The exposure index reflects the potential intensity of the system facing disaster threats, the sensitivity index represents the degree of response of the system to external disturbances, and the adaptability index reflects the system's self-regulation and recovery potential.

[0094] Specifically, the calculation formula is as follows:

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] Among them, E d 、S d 、AC d They represent the exposure index E, sensitivity index S, adaptability index AC, and FVI corresponding to dimension d. d represents the vulnerability index of dimension d, x Ed,j,norm represents the standardized value of the jth coastal flood vulnerability evaluation index included in the exposure index E corresponding to dimension d, x Sd,j,norm represents the standardized value of the jth coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d, xACd,j,norm represents the standardized value of the jth 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 jth coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, w Sd,j,norm represents the normalized weight of the jth coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d, w ACd,j,norm It represents the normalized weight of the j-th coastal flood vulnerability evaluation index included in the adaptive capacity index AC corresponding to dimension d.

[0100] The vulnerability index is calculated as the product of exposure and sensitivity divided by adaptive capacity. This mathematical form reflects a dynamic equilibrium relationship in which vulnerability increases with the strength of the first two and decreases with the strength of the second. This formula better aligns with the multi-factor nonlinear coupling mechanism in actual disaster scenarios. When adaptive capacity is insufficient, even small changes in exposure and sensitivity can trigger significant fluctuations in vulnerability. By calculating the vulnerability index in different dimensions, it is possible to identify the different vulnerability contributions of different system elements, providing directional guidance for the development of targeted disaster prevention measures.

[0101] S5. Obtain the composite vulnerability index of each grid unit based on the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension.

[0102] Specifically, the calculation formula is as follows:

[0103] ;

[0104] 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.

[0105] The construction of a composite vulnerability index requires balancing the spatial heterogeneity of multidimensional assessment results. The geometric mean method integrates multidimensional data through product root operations. Compared to the arithmetic mean, it can reduce the excessive impact of extreme values ​​on the overall assessment results. The multiplication relationship between the four dimensional indices requires that each dimension maintains a low vulnerability in order for the overall index to be low. High vulnerability in any dimension will significantly increase the composite index value. The design of this formula conforms to the theory of the weak link effect of disaster systems. The geometric mean process makes the composite index equally sensitive to changes in each dimension. When the index of a dimension changes by an order of magnitude, the root operation can balance its impact intensity. While preserving the independent contribution of each dimension, this method emphasizes the overall correlation of system vulnerability, preventing a single dominant dimension from masking the risks and hidden dangers of other dimensions.

[0106] S6. Conduct spatial analysis of the composite vulnerability index to generate a map of coastal vulnerability hotspots.

[0107] In some embodiments, S6 uses Getis-Ord Methods Spatial analysis of the composite vulnerability index was conducted, including the following sub-steps:

[0108] S61. Conduct hotspot analysis based on the composite vulnerability index of each grid cell;

[0109] The calculation formula is as follows:

[0110] ;

[0111] in, represents the Getis-Ord of the i-th grid cell Statistics, 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. For example, it can be defined based on proximity. For example, when grid cell p is adjacent to grid cell i, then w ip is 1, otherwise w ip 0; FVI p represents the composite vulnerability index of the p-th grid cell, represents the average value of the composite vulnerability index of all grid cells, and s represents the standard deviation of the composite vulnerability index of all grid cells.

[0112] S62, according to Getis-Ord Statistics are used to generate a hotspot map of coastal vulnerability.

[0113] Among them, if If it is a positive value, it means that the area is a hot spot with high vulnerability. A negative value indicates that the area is a cold spot with low vulnerability.

[0114] The resulting hotspot map can visually demonstrate the spatial differentiation of vulnerability through color gradients. Using spatial heterogeneity identification techniques, it can provide a basis for the development of differentiated disaster prevention strategies, prioritizing areas of concentrated vulnerability and analyzing their formation mechanisms, thereby improving the spatial targeting of disaster management and the efficiency of resource allocation. The resulting spatial distribution map not only reveals the current situation but also analyzes vulnerability evolution trends through comparative analysis of historical data, establishing a spatial benchmark framework for long-term monitoring and dynamic assessment.

[0115] This paper, based on the construction of a four-dimensional indicator system, integrates social, economic, physical, and environmental multi-dimensional factors, breaking through the limitations of traditional single-dimensional evaluation and comprehensively considering the complex vulnerability characteristics of coastal systems. Principal component analysis is used to decompose and extract the principal components of the data. Indicator weights are dynamically assigned by combining the factor loading matrix and the eigenvalue ratio, effectively eliminating indicator redundancy and addressing the subjectivity of traditional expert weighting, thus achieving objective quantification of evaluation weights. The geometric mean model integrates multi-dimensional vulnerability indices, avoiding the arithmetic mean's sensitivity to extreme values ​​and accurately reflecting the weak-board effect of system vulnerability. Spatial hotspot analysis, combined with a geographic weight matrix, identifies the spatial clustering patterns of vulnerability through local spatial autocorrelation statistics, revealing complex hotspot areas characterized by high exposure, high sensitivity, and low adaptability. This approach achieves a transition from single-indicator superposition to multi-dimensional coupled analysis, from static weight assignment to data-driven weighting, and from planar evaluation to spatial heterogeneity identification, providing multi-dimensional decision support for the precise identification of disaster risks and the optimization of spatial planning.

[0116] The embodiment of the present invention further provides a coastal flood vulnerability assessment system for executing the above-mentioned coastal flood vulnerability assessment method. Figure 2 This is a schematic diagram of the system structure of a coastal flood vulnerability assessment method provided by an embodiment of the present invention. Figure 2 , the system includes the following modules:

[0117] An evaluation system construction module is used to determine coastal flood vulnerability evaluation indicators and construct a four-dimensional indicator system consisting of social vulnerability, economic vulnerability, physical vulnerability, and environmental vulnerability dimensions, where each dimension includes exposure indicators, sensitivity indicators, and adaptive capacity indicators;

[0118] The standardization processing module is connected to the evaluation system construction module and 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 standardize the actual values ​​of each coastal flood vulnerability evaluation index;

[0119] The weight calculation module is connected to the standardization processing module and is used to perform principal component analysis on each evaluation index after standardization processing, and determine the weight of each evaluation index according to the obtained eigenvalue and factor load matrix;

[0120] 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 based on the weight of each evaluation indicator and the standardized value of each evaluation indicator; and to obtain a composite vulnerability index of the target area based on the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension and environmental vulnerability dimension;

[0121] The hotspot map generation module is connected to the vulnerability index calculation module and is used to perform spatial analysis on the composite vulnerability index and generate a coastal vulnerability hotspot area map.

[0122] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A coastal flood vulnerability assessment method, characterized in that: The method comprises the following steps: S1. Determine coastal flood vulnerability assessment indicators and construct a four-dimensional indicator system comprising social vulnerability, economic vulnerability, physical vulnerability, and environmental vulnerability dimensions, wherein each dimension includes an exposure index, a sensitivity index, and an adaptability index, and each of the exposure index, the sensitivity index, and the adaptability index includes several coastal flood vulnerability assessment indicators; S2. Obtain coastal flood data, geographic information data, and socioeconomic data of the target area, divide the target area into a number of grid cells, extract the actual value of each coastal flood vulnerability assessment index in each grid cell, and standardize the actual value of each coastal flood vulnerability assessment index; S3. Perform principal component analysis on each evaluation indicator after standardization, and determine the weight of each evaluation indicator based on the obtained eigenvalue and factor loading matrix; S4. Calculate the vulnerability index of each dimension of each grid unit according to the weight of each evaluation indicator and the standardized value of each evaluation indicator; S5. Obtaining a composite vulnerability index of each grid unit according to the vulnerability indices of the social vulnerability dimension, the economic vulnerability dimension, the physical vulnerability dimension, and the environmental vulnerability dimension; S6. Perform spatial analysis on the composite vulnerability index to generate a coastal vulnerability hotspot map.

2. A coastal flood vulnerability assessment method according to claim 1, characterized in that: In S2, the standardization of the actual values ​​of the coastal flood vulnerability assessment indicators includes: The flood vulnerability evaluation indicators of each coast are divided into positive indicators and negative indicators; The calculation formula for normalizing the positive indicators is as follows: ； The calculation formula for normalizing the reverse indicator is as follows: ； Among them, x j represents the actual value of the jth coastal flood vulnerability evaluation index, μ j represents the arithmetic mean of the jth coastal flood vulnerability evaluation index, σ j represents the standard deviation of the jth coastal flood vulnerability assessment index, x j,norm represents the standardized value of the j-th coastal flood vulnerability assessment index.

3. A coastal flood vulnerability assessment method according to claim 1, characterized in that: In S3, principal component analysis is performed on each evaluation index after standardization, and the weight of each evaluation index is determined based on the obtained eigenvalue and factor loading matrix, including: S31, constructing a covariance matrix based on the standardized evaluation indicators; S32, performing eigenvalue decomposition according to the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and retaining eigenvalues ​​greater than 1 as 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: ； Among them, L jk represents the load of the jth coastal flood vulnerability evaluation index on the kth principal component, λ k represents the eigenvalue of the kth principal component, a qk Represents the qth element in the eigenvector corresponding to the kth principal component; S34. Calculate the weight of each evaluation indicator based on the eigenvalue of the principal component and the factor loading matrix.

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

5. A coastal flood vulnerability assessment method according to claim 1, characterized in that: In S4, the vulnerability index of each dimension of each grid unit is calculated according to the weight of each evaluation index and the standardized value of each evaluation index. The calculation formula is as follows: ； ； ； ； Among them, E d 、S d 、AC d They represent the exposure index E, sensitivity index S, adaptability index AC, and FVI corresponding to dimension d. d represents the vulnerability index of dimension d, x Ed,j,norm represents the standardized value of the jth coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, x Sd,j,norm represents the standardized value of the jth coastal flood vulnerability evaluation index included in the sensitivity index S corresponding to dimension d, x ACd,j,norm represents the standardized value of the jth 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 jth coastal flood vulnerability assessment index included in the exposure index E corresponding to dimension d, w Sd,j,norm represents the normalized weight of the jth coastal flood vulnerability assessment 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 assessment index included in the adaptability index AC corresponding to dimension d.

6. A coastal flood vulnerability assessment method according to claim 5, characterized in that: In S5, the composite vulnerability index of each grid unit is obtained 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. A coastal flood vulnerability assessment method according to claim 1, characterized in that: In S6, performing spatial analysis on the composite vulnerability index to generate a coastal vulnerability hotspot map includes: S61. Conduct hotspot analysis based on the composite vulnerability index of each grid cell; The calculation formula is as follows: ； in, represents the Getis-Ord of the i-th grid cell Statistics, 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 index of all grid cells, and s represents the standard deviation of the composite vulnerability index of all grid cells; S62, according to Getis-Ord Statistics are used to generate a hotspot map of coastal vulnerability.

8. A coastal flood vulnerability assessment system, used to implement the coastal flood vulnerability assessment method according to any one of claims 1 to 7, characterized in that: The system includes the following modules: An evaluation system construction module is used to determine coastal flood vulnerability evaluation indicators and construct a four-dimensional indicator system including social vulnerability, economic vulnerability, physical vulnerability, and environmental vulnerability dimensions, wherein each dimension includes an exposure index, a sensitivity index, and an adaptability index, and the exposure index, the sensitivity index, and the adaptability index each include several coastal flood vulnerability evaluation indicators; a standardization processing module, connected to the evaluation system construction module, for obtaining coastal flood data, geographic information data, and socioeconomic data of the target area, extracting the actual value of each coastal flood vulnerability evaluation index, and performing standardization processing on the actual value 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 evaluation index after standardization processing, and determine the weight of each evaluation index based on the obtained eigenvalue and factor load matrix; a vulnerability index calculation module, connected to the standardization processing module and the weight calculation module, for calculating the vulnerability index of each dimension based on the weight of each evaluation indicator and the standardized value of each evaluation indicator; and obtaining a composite vulnerability index of the target area based on the vulnerability indices of the social vulnerability dimension, economic vulnerability dimension, physical vulnerability dimension, and environmental vulnerability dimension; The hotspot map generation module is connected to the vulnerability index calculation module and is used to perform spatial analysis on the composite vulnerability index to generate a coastal vulnerability hotspot area map.

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