A method for fine assessment of marine disaster risk in coastal areas

Abstract

The application discloses a kind of coastal areas marine disaster risk fine evaluation research method, comprising the following steps:1.risk factor identification, evaluation index selection and data acquisition;2.AHP method is used to analyze each index weight;3.Lawson algorithm and Ordinary Kriging interpolation method are used to realize fine research;4.risk assessment model is constructed, and the risk value of each evaluation unit is calculated;5.risk assessment chart and risk zoning chart are drawn.The application comprehensively considers the natural attribute and social attribute of marine disaster, considers the risk of marine disaster based on the four dimensions of hazard, vulnerability, exposure and defense, which is convenient for index evaluation calculation.The application constructs index evaluation system based on AHP, uses Lawson algorithm and Ordinary Kriging interpolation to realize fine, realizes the fine evaluation of marine disaster risk, and improves the evaluation accuracy.

Description

Technical Field

[0001] This invention relates to the field of marine disaster risk assessment research methods, and in particular to a refined research method for marine disaster risk assessment in coastal areas. Background Technology

[0002] In recent years, marine disasters such as typhoons and storm surges have occurred frequently, causing huge economic losses and posing significant risks to the lives and production safety of residents in coastal areas. Therefore, conducting detailed research on marine disaster risk assessment is extremely important to support disaster prevention and mitigation efforts in coastal areas and ensure the safety of residents' lives and production.

[0003] Currently, the mainstream risk assessment research methods mainly include mathematical modeling and indicator-based assessment. Among them, mathematical modeling studies mainly use mathematical models to study the storm surge increase brought by typhoons of different intensities and draw inundation range maps to express marine disaster risks. However, it is difficult to comprehensively consider social factors and its applicability is not high in economically developed and densely populated coastal areas. Indicator-based assessment methods currently mostly construct assessment indicator systems to achieve marine disaster risk assessment at the city scale.

[0004] Current research on indicator assessments is often too broad in scale, failing to accurately reflect the risk differences between different areas within a city, which brings many inconveniences to practical applications.

[0005] Therefore, through beneficial exploration and research, the applicant has found a solution to the above problems, and the technical solution to be introduced below is the result of this research. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a refined assessment method for marine disaster risks in coastal areas that improves the accuracy of assessment and is easy to apply in practice, in order to address the shortcomings of existing technologies.

[0007] The technical problem to be solved by this invention can be achieved by the following technical solution:

[0008] A method for refined assessment of marine disaster risks in coastal areas includes the following steps:

[0009] Step S10: Risk factor identification, assessment indicator selection, and data acquisition:

[0010] Based on the characteristics of the coastal areas being assessed, major historical marine disasters were investigated, and risk factors were comprehensively determined by combining data and information. Marine disasters possess both natural and social attributes, and their risks can be comprehensively defined by four dimensions: hazard, vulnerability, exposure, and defensibility.

[0011] Step S20: Analyze the weights of each indicator using the AHP method;

[0012] The pairwise comparisons of each evaluation indicator yielded the result a. ij The matrix form of all results is

[0013]

[0014] Construct the judgment matrix and find its largest eigenvalue λ. max And the consistency of the judgment matrix is ​​checked:

[0015] CR=CI / / RI (2)

[0016] in, RI is a sequence of 500 randomly generated samples, and the CI value corresponding to the largest eigenvalue is calculated. If the CR values ​​of both a single layer and the total layers are less than 0.1, the judgment matrix can be considered to meet the consistency review.

[0017] Step S30: A refined study is performed using the Lawson algorithm and Ordinary Kriging interpolation method.

[0018] Ordinary Kriging interpolation posits that the value of a variable can be estimated using a linear combination of observations from a series of points in a region, i.e.:

[0019]

[0020] To satisfy the unbiasedness and optimality of interpolation, that is:

[0021] E(Z(X0)-Z * (X0))=0 (4)

[0022] D(Z(X0)-Z * (X0))=min (5)

[0023] In the formula: E is the expected value; D is the variance; min is the minimum value; λ i The coefficients can be obtained by solving the (n+1)th order Kriging linear equations, i.e.:

[0024]

[0025] Where C ij It can be determined by the variogram γ(h)

[0026] The Lawson algorithm can be achieved by constructing a large triangle or polygon to enclose all data points, inserting a point into it, and connecting this point to the three vertices of the triangle containing it to form three new triangles. Then, empty circumcircle detection is performed on each of them. At the same time, the Local Optimization Procedure (LOP) is used to ensure that the resulting triangular mesh is a Delaunay triangular mesh.

[0027] In spatial interpolation, the variation function in the one-dimensional case is calculated for each index in both the x and y directions, i.e.:

[0028]

[0029] There is a certain relationship between the variogram and the distance h of the random variable, which can be represented by a theoretical model. This study adopts a spherical model:

[0030] In the formula: a is the range, that is, the region where the variables are correlated; h is the lag distance; c is the sill value;

[0031] Step S40: Construct a risk assessment model and calculate the risk value for each assessment unit.

[0032] Because the dimensions and magnitudes of the various indicators are inconsistent, standardization is required to better serve subsequent calculations and evaluations.

[0033] In the formula, y i MAX represents the standardized value of indicator x for the i-th evaluation unit; MIN represents the maximum value of indicator x for all evaluation units; N represents the minimum value of indicator x for all evaluation units; N is the quantification parameter.

[0034] Risk assessment model

[0035] Where W is the risk index, a is the weight of risk indicator i, and n is the number of indicators;

[0036] Vulnerability assessment model

[0037] Where C is the vulnerability index, b is the weight of vulnerability indicator i, and n is the number of indicators;

[0038] Exposure assessment model

[0039] Where B is the exposure index, c is the weight of exposure indicator i, and n is the number of indicators;

[0040] Defensive assessment model

[0041] Where F is the defensive index, d is the weight corresponding to defensive indicator i, and n is the number of indicators;

[0042] Risk index assessment model:

[0043] In the formula, α, β, γ, and δ are the weighting coefficients of the risk, vulnerability, exposure, and defensiveness indices, respectively;

[0044] Step S50: Draw the risk assessment map and risk zoning map:

[0045] Based on the risk value of each assessment unit, risk assessment and risk zoning maps are drawn using GIS tools.

[0046] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0047] 1. This invention comprehensively considers the natural and social attributes of marine disasters and proposes a comprehensive approach to marine disaster risk assessment based on four dimensions: hazard, vulnerability, exposure, and defensibility, which facilitates indicator evaluation and calculation.

[0048] 2. This invention constructs an index evaluation system based on AHP, and uses Lawson algorithm and Ordinary Kriging interpolation to achieve refinement. Through a comprehensive risk assessment model, it realizes a refined assessment of marine disaster risks and improves the accuracy of the assessment. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart of the present invention.

[0051] Figure 2 This is a flowchart of an application embodiment of the present invention.

[0052] Figure 3 This is Shanghai's marine disaster risk assessment indicator system.

[0053] Figure 4 This is a schematic diagram of the Lawson algorithm of the present invention.

[0054] Figure 5 This is a schematic diagram of the refined evaluation nodes of the present invention.

[0055] Figure 6 This is a schematic diagram of the refined assessment of marine disaster risks in Shanghai.

[0056] Figure 7 This is a schematic diagram of the refined assessment and zoning of marine disaster risks in Shanghai. Detailed Implementation

[0057] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below with reference to specific illustrations.

[0058] See Figure 1 The figure illustrates a method for refined assessment of marine disaster risks in coastal areas, including the following steps:

[0059] Step S10: Risk factor identification, assessment indicator selection, and data acquisition:

[0060] Based on the characteristics of the coastal areas being assessed, major historical marine disasters were investigated, and risk factors were comprehensively determined by combining relevant data. Marine disasters possess both natural and social attributes, and their risks can be comprehensively defined by four dimensions (primary indicators): hazard, vulnerability, exposure, and defensibility. Each dimension can be further subdivided into assessment indicators to construct an assessment system.

[0061] Step S20: Analyze the weights of each indicator using the AHP method:

[0062] The method for constructing the judgment matrix in the Analytic Hierarchy Process (AHP) is the consistent matrix method, which means that instead of comparing all factors together, each factor is compared pairwise. A relative scale is used in this case to minimize the difficulty of comparing factors with different properties, thereby improving accuracy. Pairwise comparisons are performed on each evaluation index, and the result is a. ij The matrix form of all results (judgment matrix) is as follows:

[0063]

[0064] Construct the judgment matrix and find its largest eigenvalue λ. max And the consistency of the judgment matrix is ​​checked:

[0065] CR=CI / / RI (2)

[0066] in, RI is a sequence of 500 randomly generated samples, for which the CI value corresponding to the largest eigenvalue is calculated. If the CR values ​​of both a single layer and the overall layer are less than 0.1, the judgment matrix can be considered to meet the consistency review.

[0067] Step S30: A refined study is performed using the Lawson algorithm and Ordinary Kriging interpolation method.

[0068] Ordinary Kriging is a type of Kriging interpolation method. It is a regression algorithm that interpolates stochastic processes based on the covariance function. This method assumes that the values ​​of variables can be estimated using a linear combination of observations from a series of points in a region, i.e.:

[0069]

[0070] To satisfy the unbiasedness and optimality of interpolation, that is:

[0071] E(Z(X0)-Z * (X0))=0 (4)

[0072] D(Z(x0)-Z * (X0))=min (5)

[0073] In the formula: E is the expected value; D is the variance; min is the minimum value; λ i The coefficients can be obtained by solving the (n+1)th order Kriging linear equations, i.e.:

[0074]

[0075] Where C ij It can be determined by the variogram γ(h).

[0076] The Lawson algorithm, proposed by Lawson in 1977, works by creating a large triangle or polygon that encloses all data points. A point is then inserted into this triangle and connected to the three vertices of the containing triangle, forming three new triangles. Each triangle is then checked for empty circumcircles. Meanwhile, the Local Optimization Procedure (LOP) method, which swaps diagonals, ensures that the resulting triangular mesh is a Delaunay triangular mesh.

[0077] In spatial interpolation, the variation function in the one-dimensional case is calculated for each index in both the x and y directions, i.e.:

[0078]

[0079]

[0080] There is a certain relationship between the variogram and the distance h of the random variable, which can be represented by a theoretical model. This study adopts a spherical model:

[0081] In the formula: a is the range, that is, the region where the variables are correlated; h is the lag distance; and c is the sill value.

[0082] Step S40: Construct a risk assessment model and calculate the risk value for each assessment unit.

[0083] Because the dimensions and magnitudes of the various indicators are inconsistent, standardization is required to better serve subsequent calculations and evaluations.

[0084] In the formula, y i MAX is the standardized value of indicator x for the i-th evaluation unit; MIN is the maximum value of indicator x for all evaluation units; N is the quantification parameter, which is set to N=4.

[0085] Risk assessment model

[0086] Where W is the risk index, a is the weight of risk indicator i, and n is the number of indicators.

[0087] Vulnerability assessment model

[0088] Where C is the vulnerability index, b is the weight of vulnerability indicator i, and n is the number of indicators.

[0089] Exposure assessment model

[0090] Where B is the exposure index, c is the weight of exposure indicator i, and n is the number of indicators.

[0091] Defensive assessment model

[0092] Where F is the defensive index, d is the weight corresponding to defensive indicator i, and n is the number of indicators.

[0093] Risk index assessment model:

[0094] In the formula, α, β, γ, and δ are the weighting coefficients of the risk, vulnerability, exposure, and defensiveness indices, respectively.

[0095] Step S50: Draw the risk assessment map and risk zoning map:

[0096] Based on the risk value of each assessment unit, risk assessment and risk zoning maps are drawn using GIS tools.

[0097] To better illustrate or explain the technical solution of this invention, the following uses Shanghai as an example to conduct a refined assessment and zoning study of marine disaster risks. The main marine disasters are storm surges and waves. When Typhoon "Hwamak" (No. 6 of 2021) made landfall, it coincided with a spring tide, and Shanghai issued a red alert for storm surge exceeding the warning level. Water levels at stations such as Mishidu on the upper reaches of the Huangpu River reached record highs. The causative factors of marine disasters such as storm surges include two aspects: the subject, which includes disaster-causing factors such as extreme weather events like typhoons and sea-level rise under the influence of climate change; and the object, which includes the disaster-prone environment, including the level and compliance of dikes, distance from the shoreline, and altitude.

[0098] like Figure 1 As shown, the method for refined assessment of marine disaster risks in coastal areas in this embodiment includes the following steps:

[0099] Step 1: Risk factor identification, evaluation indicator selection and data acquisition.

[0100] Four primary indicators were selected: hazard index, exposure index, vulnerability index, and defense index. Each primary indicator corresponds to several secondary indicators. The hazard index considers three aspects: disaster-causing factors, defense level, and disaster risk. Secondary indicators selected include sea-level rise, storm surge, defense level, closest distance to the coastline, closest distance to the Huangpu River, and ground elevation. The exposure index selected total population and total GDP as secondary indicators. Vulnerability selected population density and economic density as secondary indicators. Disaster prevention and mitigation capacity selected the proportion of employed population and fiscal revenue as secondary indicators. Figure 3 As shown.

[0101] Step 2: Use the AHP method to analyze the weights of each indicator.

[0102] in accordance with Figure 3 The indicator system requires the construction of judgment matrices for each primary indicator and the six secondary indicators under the risk index, as shown in Tables 1 and 2. The maximum eigenvalue of the judgment matrix for each primary indicator is 4.047, the consistency index (CI) is 0.0158, and the consistency ratio (CR) is 0.0175 < 0.1, meeting the consistency test. The weights of each indicator are shown in the first column of Table 3. The maximum eigenvalue of the judgment matrix for each secondary indicator under the risk index is 6.127, the consistency index (CI) is 0.0253, and the consistency ratio (CR) is 0.0204 < 0.1, meeting the consistency test. The weights of each indicator are shown in the third column of Table 3.

[0103] Table 1. Primary Indicator Judgment Matrix

[0104] index Risk Index Exposure index Vulnerability Index Defensiveness Index Risk Index 1 5 4 5 Exposure index 1 / 5 1 1 / 2 1 / 2 Vulnerability Index 1 / 4 2 1 1 Defensiveness Index 1 / 5 2 1 1

[0105] Table 2 Judgment Matrix of Each Indicator under the Risk Index

[0106]

[0107]

[0108] Table 3. Sources, scales, and weights of each indicator.

[0109]

[0110] Step 3: The Lawson algorithm and Ordinary Kriging interpolation are used to achieve a refined study.

[0111] Based on the Lawson algorithm, a Delaunay grid with a side length not exceeding 200m was generated, generalizing the Shanghai area (including the mainland and Chongming Islands) to 220,000 nodes. The local node distribution is as follows: Figure 4 As shown.

[0112] For all indices to be interpolated, λ is determined using equations (6) and (7)-(9). i Then, by using equation (3), all indicator data are interpolated at each node, and by using equation (10) to standardize the data, the sequence of indicator data for all nodes is obtained.

[0113] Step 4: Construct a risk assessment model and calculate the risk value of each assessment unit.

[0114] For each node, its risk index, vulnerability index, exposure index, and defense index are calculated according to equations (11)-(14), and finally the risk index is calculated using equation (15). The central urban area has the highest risk; the areas along the south bank of the Yangtze River estuary and the north bank of Hangzhou Bay are medium to high risk areas; the central Pudong, Jiading, and southeast corner of Chongming are light to moderate risk areas; the central and northern Chongming and northwestern Shanghai, including Qingpu, are low risk areas. Areas far from the shoreline, with higher elevation, sparse population, and weak economy generally have lower risk. In the densely populated and economically concentrated central urban area, the risk is higher than in some areas closer to the shoreline, such as Figure 5 As shown.

[0115] Step 5: Use GIS tools to draw risk assessment maps and risk zoning maps.

[0116] Based on the risk value of each assessment unit, a risk assessment map and a risk zoning map are drawn using GIS tools, such as... Figure 6 and Figure 7 As shown.

[0117] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for fine assessment of marine disaster risk in coastal areas, characterized in that, The method comprises the following steps: Step S10, risk factor identification, evaluation index selection and data acquisition: According to the characteristics of the coastal area, the main marine disasters in history are investigated, and the risk factors are determined comprehensively in combination with data information; marine disasters have both natural and social attributes, and the risk is defined by four dimensions of danger, vulnerability, exposure and defense; Step S20, using AHP method to analyze the weight of each index: The results of pairwise comparison of each evaluation index are a ij The matrix form of all results is （1） Constructing the judgment matrix and finding its maximum eigenvalue λ max and checking the consistency of the judgment matrix: （2） wherein, , RI is the 500 sample sequences generated randomly, and the CI value corresponding to the maximum eigenvalue is calculated; if the CR values of the single layer and the total layer are less than 0.1, it is considered that the judgment matrix meets the consistency review; Step S30, using Lawson algorithm and Ordinary Kriging interpolation method to realize fine research: Ordinary Kriging interpolation method believes that according to a series of point observations in the region, a linear combination is used to estimate the value of the variable, that is: (3) Satisfy the unbiasedness and optimality of interpolation, that is, require: (4) (5) where: E is the mathematical expectation; D is the variance; min is the minimum value; are coefficients, which are obtained by solving the Kriging linear equations of n+1 order, i.e.: (6) wherein determined by the variogram function determined Lawson algorithm surrounds all data points by establishing a large triangle or polygon, inserts a point into it, connects the point with the three vertices of the triangle containing it to form three new triangles, and then detects the empty circumscribed circle of each of them, and at the same time, through the exchange of the local optimization method LOP(Local Optimization Procedure) of the diagonal line, the formed triangular mesh is a Delaunay triangular mesh; In spatial interpolation, the variation function in one-dimensional case is calculated in x and y directions for each index, that is: (7) (8) The distance h of the random variable has a certain relationship with the variation function, which is expressed by a theoretical model, and a spherical model is adopted: (9) wherein: is the range of variability, i.e. the range of values in which the variables have a correlation; h is the hysteresis distance; c is the base value. Step S40, constructing risk assessment model and calculating risk value of each evaluation unit: Because the dimensions and orders of magnitude of each index are inconsistent, standardization processing is needed to better serve subsequent calculation and evaluation work: (10) where y i is the normalized value of the i-th evaluation unit indicator x; MAX is the maximum value of all evaluation unit indicators x; MIN is the minimum value of all evaluation unit indicators x; and N is a quantization parameter. Danger assessment model (11) Wherein, W is the danger index, a is the weight corresponding to the danger index i, and n is the number of indexes; Vulnerability assessment model (12) Wherein, C is the vulnerability index, b is the weight corresponding to the vulnerability index i, and n is the number of indexes; Exposure assessment model (13) Wherein, B is the exposure index, c is the weight corresponding to the exposure index i, and n is the number of indexes; Defense assessment model (14) Wherein, F is the defense index, d is the weight corresponding to the defense index i, and n is the number of indexes; Risk index assessment model: (15) wherein are the weight coefficients of the risk, vulnerability, exposure and defense indices, respectively. Step S50, drawing risk assessment map and risk zoning map: According to the risk value of each evaluation unit, the risk assessment and risk zoning map are drawn by using GIS tool.

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