Regional geological disaster vulnerability assessment method, device, equipment and medium

Abstract

The invention discloses a regional geological disaster vulnerability evaluation method, device and equipment and a medium, and relates to the technical field of geological disaster evaluation, and the method comprises the following steps: obtaining a plurality of evaluation indexes used for evaluating the vulnerability of a geological disaster in a target region; dividing the terrain of the target area into a plurality of grids, and obtaining a plurality of evaluation index values of the corresponding terrain in each grid; fitting the plurality of evaluation index values in each grid to obtain a vulnerability value of each grid; taking the discourse domain in the cloud model as an abscissa and the membership degree as an ordinate, and constructing a standard cloud picture for representing the vulnerability level; inputting the vulnerability value of each grid into the cloud model to generate a sample cloud picture, comparing the membership degree of the sample cloud picture with a plurality of membership degrees of a standard cloud picture to obtain a comparison result, and obtaining the vulnerability level of the target area according to the comparison result. According to the method, fuzziness and randomness can be reflected through the difference value of the membership degrees, and the accuracy of vulnerability evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster assessment, and in particular to a method, device, equipment and medium for regional geological disaster vulnerability assessment. Background Art

[0002] The concept of vulnerability is used in many fields and at different spatial levels. In geological hazard research, vulnerability is defined as characteristics associated with specific geological hazards, including losses in all aspects of individuals and groups, highlighting the relationship between society and nature. At present, research on regional vulnerability assessment is mainly reflected in assessment models and methods. With the continuous iterative development of technology, machine learning methods are also being used in the process of vulnerability assessment. In addition to improving the model, there are also attempts to use other methods to improve the results of vulnerability assessment. The above methods effectively enhance the accuracy of vulnerability assessment results and are applicable to many aspects involved in vulnerability assessment.

[0003] However, in vulnerability assessments, detailed assessments are often conducted within the region, while for disaster prevention and mitigation, policies are usually formulated based on the administrative district scale, which makes it impossible to achieve an overall assessment of the region.

[0004] For the vulnerability assessment of regional geological hazards, there are currently methods such as multi-factor composite assessment method and fuzzy comprehensive assessment. Multi-factor composite assessment method and fuzzy comprehensive assessment method are two commonly used methods. Multi-factor composite assessment method comprehensively considers many influencing factors of vulnerability, and uses classification methods to find the most representative factors in the hazard-bearing body that have the greatest impact on vulnerability. By setting different disaster-causing environments and hazard-bearing bodies, the assessment indicators are optimized and finally the vulnerability assessment results are obtained. Although the multi-factor composite assessment method can comprehensively consider various factors of geological hazard vulnerability, there is a great deal of subjectivity in the selection of population indicators and property indicators. This subjectivity may lead to instability and inconsistency in the assessment results, thereby affecting the formulation and implementation of disaster prevention and mitigation measures. In addition, this method has deficiencies in dealing with the fuzziness and randomness of the assessment factors, and it is difficult to accurately reflect the true situation of geological hazard vulnerability. Fuzzy comprehensive assessment method is based on the principles of fuzzy theory and the maximum membership principle, and obtains a scientific vulnerability assessment through comprehensive analysis of the specimens from multiple aspects. This method converts qualitative assessment into quantitative assessment by setting fuzzy sets and membership functions, effectively reducing the influence of human factors or uncertain factors. The fuzzy comprehensive evaluation method has certain advantages in dealing with the fuzziness problem in the vulnerability assessment of geological hazards, but the accuracy of its evaluation depends largely on the acquisition of the evaluation index and whether the boundary function is set reasonably. If the evaluation index data is inaccurate or the boundary function is set improperly, it will directly affect the accuracy and reliability of the evaluation results. In addition, this method also has certain limitations in dealing with the randomness of the evaluation factors.

[0005] In summary, the existing vulnerability assessment methods for regional geological hazards are often refined assessments within the region, and cannot achieve overall assessment of the region. At the same time, the existing assessment methods need to assess the accuracy of vulnerability based on the degree of acquisition of assessment indicators, but the assessment indicators are random and fuzzy, which reduces the accuracy of the assessment and makes it difficult to accurately reflect the true situation of geological hazard vulnerability. Summary of the invention

[0006] The present invention provides a method, device, equipment and medium for assessing the vulnerability of regional geological disasters, which solves the problem that the existing method for assessing the vulnerability of regional geological disasters cannot achieve an overall assessment of the region and the assessment accuracy is too low, making it difficult to accurately reflect the true situation of the vulnerability of geological disasters.

[0007] In a first aspect, the present invention provides a method for assessing regional geological hazard vulnerability, comprising the following steps:

[0008] Obtain multiple evaluation indicators for assessing the vulnerability of target areas to geological hazards;

[0009] Divide the terrain of the target area into multiple grids, and obtain multiple evaluation index values ​​of the corresponding terrain in each grid; fit the multiple evaluation index values ​​in each grid to obtain the vulnerability value of each grid;

[0010] With the domain in the cloud model as the horizontal axis and the membership degree as the vertical axis, a standard cloud map for characterizing the vulnerability level is constructed;

[0011] The vulnerability value of each grid is input into the cloud model to generate a sample cloud map. The membership of the sample cloud map is compared with multiple memberships of the standard cloud map to obtain the membership comparison result. The vulnerability level of the target area is divided according to the membership comparison result.

[0012] Preferably, the multiple evaluation indicators include three first-level rating indicators of exposure, sensitivity, and corresponding measures and corresponding multiple second-level evaluation indicators.

[0013] Preferably, the obtaining of multiple evaluation index values ​​in each grid comprises the following steps:

[0014] Obtaining a first weight vector of each weight calculation method for multiple secondary evaluation indicators, and combining multiple first weight vectors to obtain a weight vector set;

[0015] Converting the weight vector set into a vector to be optimized, where the vector to be optimized is the sum of products of a plurality of first weight vectors and corresponding first weight coefficients;

[0016] Minimizing the difference between the vector to be optimized and the multiple first weight vectors is used as the optimization target, obtaining the optimal first-order derivative condition of the optimization target and normalizing the multiple second weight coefficients contained therein to obtain the optimal weight coefficients of the multiple secondary evaluation indicators;

[0017] The normalized vector of multiple secondary evaluation index values ​​in each grid is obtained, and the normalized vector of multiple secondary evaluation index values ​​of the primary rating index is multiplied by the corresponding optimal weight coefficient to obtain the primary evaluation index value.

[0018] Preferably, the normalized vectors of the multiple secondary evaluation index values ​​of the primary rating index are multiplied by the corresponding optimal weight coefficients, as shown below:

[0019]

[0020] In the formula, E is the exposure value, S is the sensitivity value, C is the corresponding measure value, and ω is the normalized vector. is the combined weight vector corresponding to the secondary evaluation index under exposure, is the combined weight vector corresponding to the secondary evaluation index under sensitivity, is the combined weight vector corresponding to the secondary evaluation indicators under the corresponding measures, n is the number of secondary evaluation indicators, and T is the transposition.

[0021] Preferably, the multiple evaluation index values ​​in each grid are fitted, as shown below:

[0022]

[0023] Where FV is the vulnerability value of the current grid.

[0024] Preferably, the step of inputting the vulnerability value of each grid into the cloud model to generate a sample cloud map comprises the following steps:

[0025] The vulnerability value of each grid is input into the inverse cloud generator to obtain a plurality of corresponding three characteristic parameters;

[0026] A plurality of corresponding three characteristic parameters are input into a forward cloud generator to obtain a plurality of cloud droplets, and a sample cloud image is generated through the plurality of cloud droplets.

[0027] Preferably, the vulnerability levels of the standard cloud map include high [0.8, 1.0], relatively high [0.6, 0.8], average [0.4, 0.6], relatively low [0.2, 04] and low [0, 0.2].

[0028] In a second aspect, the present invention provides a regional geological hazard vulnerability assessment device, comprising:

[0029] An acquisition module, used to acquire multiple evaluation indicators for evaluating the vulnerability of geological hazards in a target area;

[0030] A partitioning module is used to divide the terrain of the target area into multiple grids, obtain multiple evaluation index values ​​of the corresponding terrain in each grid; fit the multiple evaluation index values ​​in each grid to obtain the vulnerability value of each grid;

[0031] A construction module is used to construct a standard cloud map for representing the vulnerability level by using the domain in the cloud model as the horizontal coordinate and the membership degree as the vertical coordinate;

[0032] The evaluation module is used to input the vulnerability value of each grid into the cloud model to generate a sample cloud map, compare the membership of the sample cloud map with multiple memberships of the standard cloud map to obtain the membership comparison result, and divide the vulnerability level of the target area according to the membership comparison result.

[0033] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned regional geological hazard vulnerability assessment method when executing the program.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned regional geological hazard vulnerability assessment method is implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention divides the target area into multiple grids, generates a sample cloud map of the entire target area by comparing the vulnerability values ​​of multiple grids, realizes the transition from the small scale of the grid to the large scale of the target area, and realizes the overall evaluation of the area. The present invention inputs the vulnerability value of each grid into the cloud model, generates a sample cloud map, compares the membership of the sample cloud map with the multiple memberships of the standard cloud map, obtains the comparison result, and obtains the vulnerability level of the target area according to the comparison result. The present invention proposes a cloud model as a method for evaluating regional vulnerability, establishes a sample cloud based on a large number of grid data in the target area, constitutes an evaluation method with cloud membership as an indicator, directly applies the grid data to the evaluation, and realizes the regional vulnerability level assessment. For the fuzziness and randomness of the evaluation index, the present invention can reflect this fuzziness and randomness through the difference of the membership. The greater the membership, the higher the accuracy, which can reduce this fuzziness and randomness and improve the accuracy of vulnerability assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flow chart of a regional geological hazard vulnerability assessment method of the present invention;

[0039] Figure 2 is a schematic diagram of a cloud generator of the present invention;

[0040] in, Figure 2 (a): Forward cloud generator, Figure 2 (b): Reverse cloud generator;

[0041] Figure 3 It is a standard cloud model diagram of the present invention;

[0042] Figure 4 A sample cloud model diagram of the vulnerability of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] A regional geological hazard vulnerability assessment method, referring to Figure 1 , including the following steps:

[0045] Step 1: Obtain multiple evaluation indicators for assessing the vulnerability of geological hazards in the target area and determine the optimal weight coefficients of multiple evaluation indicators.

[0046] The vulnerability assessment of a province in this paper highlights the impact of geological disasters on social and economic aspects and the response capabilities of individuals or groups to geological disasters. The vulnerability is divided into three primary indicators, namely exposure, sensitivity and corresponding measures, and each primary indicator is divided into three secondary indicators.

[0047] The present invention uses game theory to combine subjective and objective weights, which can effectively utilize subjective and objective weight information to obtain the best weight combination. The specific calculation steps of game theory are as follows:

[0048] Suppose there are N weight calculation methods to calculate m indicators and the weight vector obtained is:

[0049] ω i ={ω i1 ,ω i2 …ω im},i=1,2…,N (1).

[0050] Based on this, we can get the weight vector set of all method combinations: ω={ω1,ω2,…,ω N}, any linear combination of these N vectors can be obtained:

[0051]

[0052] By processing these N linear combination coefficients, the final ω and ω i The gap is the smallest, so the following model can be obtained:

[0053]

[0054] According to the differential properties of the matrix, the optimal first-order derivative condition of the above formula is:

[0055]

[0056] The coefficients (α1, α2, … α N ) is normalized to obtain the final optimal weight coefficient

[0057]

[0058] Step 2: Divide the terrain of the target area into multiple grids and obtain multiple evaluation index values ​​in each grid; fit the multiple evaluation index values ​​in each grid with the corresponding optimal weight coefficients to obtain the vulnerability value of each grid.

[0059] Divide the target area terrain into multiple grids and obtain multiple evaluation index values ​​in each grid. Calculate the normalized combination weight Then calculate each indicator data using the following formula:

[0060]

[0061] Among them, E, S and C are three first-level indicators. is the combined weight vector corresponding to the secondary indicators under exposure E, is the normalized vector of the secondary indicator value extracted from the grid. The formulas corresponding to S and C are similar. The exposure E, sensitivity S and countermeasure C values ​​of the grid are obtained respectively. The vulnerability value FV of the grid is calculated by formula (7):

[0062]

[0063] Step 3: Input the vulnerability value of each grid into the cloud model to generate a sample cloud map, compare the membership of the sample cloud map with multiple memberships of the standard cloud map used to characterize different levels of vulnerability, and obtain the vulnerability level of the target area based on the comparison results.

[0064] The cloud model can be directly used to determine the level of vulnerability assessment, which is determined by the theoretical basis. The partial definition of the cloud model is as follows:

[0065] Let X be a general set X = {x}, called the domain. About the fuzzy subsets on the domain X For any element x in the domain X, there is a number with a stable tendency element x pair The membership degree of The distribution on the domain X is called the membership cloud, and each x is called a cloud droplet, denoted by

[0066] The properties of the subordinate cloud are mainly composed of three digital features N (Ex, En, He), which are the expectation, entropy and super entropy of the cloud. The expectation represents the center of the domain, similar to the average value; entropy expresses uncertainty, similar to the concept of variance. The larger the entropy En, the wider the cloud; super entropy represents the uncertainty of entropy, and is also the cohesion of cloud droplets. The larger the super entropy He, the thicker the cloud. The three digital features of the subordinate cloud can be obtained by the following formula:

[0067]

[0068] Where S 2 is the variance.

[0069] Reference Figure 2 , the mutual conversion between qualitative and quantitative in the cloud model is realized through the cloud generator. The cloud generator is divided into a forward cloud generator and a reverse cloud generator. By inputting the digital features N (Ex, En, He) of the cloud and the number of cloud droplets into the forward cloud generator, the coordinates of each cloud droplet in the domain and the membership of each cloud droplet can be obtained. The reverse cloud generator outputs the three digital features N (Ex, En, He) of the corresponding cloud model by inputting cloud droplets that conform to a certain distribution.

[0070] Another important concept in the cloud model is cloud membership, which refers to the similarity between different clouds. The membership is calculated through the bell-shaped membership function μ(x):

[0071]

[0072] Cloud Model Regional Vulnerability Classification

[0073] The domain level is divided into five vulnerability levels [low, lower, general, higher, high], and the 3En criterion in the normal cloud model is used to represent the domain, that is, the vast majority (about 99.97%) of the quantitative values ​​that contribute to the qualitative concept in the domain fall within the interval [E x -3En,E x +3En], the parameter calculation formula corresponding to the standard cloud model is as follows:

[0074]

[0075] He = K (12);

[0076] Wherein, the super entropy K in formula (12) represents a constant that is one order of magnitude lower than En. The selection of this constant varies. In the present invention, the super entropy is set to 0.01. The parameters of each standard cloud are shown in the following table:

[0077] Table 1 Parameters of standard cloud

[0078]

[0079] The standard cloud is visualized through MATLAB. The horizontal axis represents the domain, that is, the range of vulnerability values, and the vertical axis represents the membership. 1000 standard cloud droplets are generated in MATLAB. Each standard cloud droplet is combined to form a standard cloud, such as Figure 3 As shown, from left to right there are five levels of standard clouds: low, lower, general, higher, and highly vulnerable.

[0080] The vulnerability value of each grid is considered as a cloud droplet of a cloud model Through the reverse cloud generator, three characteristic parameters N (Ex, En, He) are calculated. These three parameters can be used to determine the approximate shape of the cloud. Then, cloud droplets are randomly generated through MATLAB. Many cloud droplets together form a sample cloud. Finally, the membership between the sample cloud and the index cloud is calculated through the bell-shaped membership function of formula (9). The membership represents the similarity with a certain level. According to the principle of the highest membership value, the vulnerability level of the sample cloud is determined, as follows: Figure 4 shown.

[0081] The membership of the vulnerability sample cloud of Zhejiang Province and the five vulnerability level standard clouds are calculated respectively. After comparison, the membership of the sample cloud and the low vulnerability cloud is the highest. The smaller the scale of vulnerability evaluation, the higher the accuracy of the evaluation. Therefore, the present invention evaluates the vulnerability of each district and county in Zhejiang Province. Some areas with similar proportions of different vulnerability levels are more difficult to evaluate qualitatively, but they also reflect the advantages of cloud model evaluation.

[0082] Based on the same concept, the present invention also provides a regional geological disaster vulnerability assessment device, which includes an acquisition module, a division module, a construction module and an assessment module.

[0083] The acquisition module is used to obtain multiple evaluation indicators for evaluating the vulnerability of geological hazards in the target area.

[0084] The division module is used to divide the terrain of the target area into multiple grids, obtain multiple evaluation index values ​​of the corresponding terrain in each grid; fit the multiple evaluation index values ​​in each grid to obtain the vulnerability value of each grid.

[0085] The construction module is used to construct a standard cloud map for characterizing the vulnerability level, with the domain in the cloud model as the horizontal axis and the membership degree as the vertical axis.

[0086] The evaluation module is used to input the vulnerability value of each grid into the cloud model, generate a sample cloud map, compare the membership of the sample cloud map with multiple memberships of the standard cloud map, obtain the membership comparison result, and divide the vulnerability level of the target area according to the membership comparison result.

[0087] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned regional geological hazard vulnerability assessment method is implemented.

[0088] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned regional geological hazard vulnerability assessment method is implemented.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0090] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A regional geological hazard vulnerability assessment method, characterized in that: The following steps are involved: Obtain multiple evaluation indicators for assessing the vulnerability of target areas to geological hazards; Divide the terrain of the target area into multiple grids, and obtain multiple evaluation index values ​​of the corresponding terrain in each grid; fit the multiple evaluation index values ​​in each grid to obtain the vulnerability value of each grid; With the domain in the cloud model as the horizontal axis and the membership degree as the vertical axis, a standard cloud map for characterizing the vulnerability level is constructed; The vulnerability value of each grid is input into the cloud model to generate a sample cloud map. The membership of the sample cloud map is compared with multiple memberships of the standard cloud map to obtain the membership comparison result. The vulnerability level of the target area is divided according to the membership comparison result.

2. A regional geological hazard vulnerability assessment method according to claim 1, characterized in that: The multiple evaluation indicators include three first-level rating indicators of exposure, sensitivity, and corresponding measures and corresponding multiple second-level evaluation indicators.

3. A regional geological hazard vulnerability assessment method according to claim 2, characterized in that: The method of obtaining multiple evaluation index values ​​in each grid includes the following steps: Obtaining a first weight vector of each weight calculation method for multiple secondary evaluation indicators, and combining multiple first weight vectors to obtain a weight vector set; Converting the weight vector set into a vector to be optimized, where the vector to be optimized is the sum of products of a plurality of first weight vectors and corresponding first weight coefficients; Minimizing the difference between the vector to be optimized and the multiple first weight vectors is used as the optimization target, obtaining the optimal first-order derivative condition of the optimization target and normalizing the multiple second weight coefficients contained therein to obtain the optimal weight coefficients of the multiple secondary evaluation indicators; The normalized vector of multiple secondary evaluation index values ​​in each grid is obtained, and the normalized vector of multiple secondary evaluation index values ​​of the primary rating index is multiplied by the corresponding optimal weight coefficient to obtain the primary evaluation index value.

4. A regional geological hazard vulnerability assessment method according to claim 3, characterized in that: The normalized vector of multiple secondary evaluation index values ​​of the primary rating index is multiplied by the corresponding optimal weight coefficient, as shown below: In the formula, E is the exposure value, S is the sensitivity value, C is the corresponding measure value, and ω is the normalized vector. is the combined weight vector corresponding to the secondary evaluation index under exposure, is the combined weight vector corresponding to the secondary evaluation index under sensitivity, is the combined weight vector corresponding to the secondary evaluation indicators under the corresponding measures, n is the number of secondary evaluation indicators, and T is the transposition.

5. A regional geological hazard vulnerability assessment method according to claim 4, characterized in that: The multiple evaluation index values ​​in each grid are fitted, as shown below: Where FV is the vulnerability value of the current grid.

6. A regional geological hazard vulnerability assessment method according to claim 1, characterized in that: The method of inputting the vulnerability value of each grid into the cloud model to generate a sample cloud map includes the following steps: The vulnerability value of each grid is input into the inverse cloud generator to obtain a plurality of corresponding three characteristic parameters; A plurality of corresponding three characteristic parameters are input into a forward cloud generator to obtain a plurality of cloud droplets, and a sample cloud image is generated through the plurality of cloud droplets.

7. A regional geological hazard vulnerability assessment method according to claim 1, characterized in that: The vulnerability levels of the standard cloud map include high [0.8, 1.0], relatively high [0.6, 0.8], average [0.4, 0.6], relatively low [0.2, 04] and low [0, 0.2].

8. A regional geological disaster vulnerability assessment device, characterized in that: include: An acquisition module, used to acquire multiple evaluation indicators for evaluating the vulnerability of geological hazards in a target area; A partitioning module is used to divide the terrain of the target area into multiple grids, obtain multiple evaluation index values ​​of the corresponding terrain in each grid; fit the multiple evaluation index values ​​in each grid to obtain the vulnerability value of each grid; A construction module is used to construct a standard cloud map for representing the vulnerability level by using the domain in the cloud model as the horizontal coordinate and the membership degree as the vertical coordinate; The evaluation module is used to input the vulnerability value of each grid into the cloud model to generate a sample cloud map, compare the membership of the sample cloud map with multiple memberships of the standard cloud map to obtain the membership comparison result, and divide the vulnerability level of the target area according to the membership comparison result.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for assessing the vulnerability of regional geological hazards as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for assessing the vulnerability of regional geological hazards described in any one of claims 1 to 7 is implemented.

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