A method for evaluating the intensity of coastal erosion based on the MABAC comprehensive algorithm

Abstract

A method for evaluating the intensity of coastal erosion based on the MABAC comprehensive algorithm, which is related to the evaluation of the intensity of coastal erosion. Standardize the evaluation unit data according to the positive and negative correlations of the evaluation index normalization; distribute the influence comparison judgment table of the evaluation index factors to multiple experts in the field, and convert the valid judgment table into a factor matrix to test the consistency; use the Decision Making Trial and Evaluation Laboratory (DEMATEL) method combined with the Analytic Network Process (ANP) to calculate the subjective weights of the evaluation indexes; use the CRITIC method to calculate the objective weights of the evaluation indexes, and optimize the subjective and objective weights by using the combination method of maximizing the range; use the BM operator and the improved MABAC method considering the short board effect to conduct a comprehensive evaluation of the intensity of coastal erosion to obtain the comprehensive evaluation results of each evaluation unit; sort and grade the results and conduct visual mapping. Make up for the subjective dependence between the evaluation index factors, increase the flexibility of the expert judgment process, and ensure the rationality, credibility and intuitiveness of the evaluation results.

Description

Technical Field

[0001] The present invention relates to the evaluation of coastal erosion intensity, and particularly to a method for evaluating coastal erosion intensity based on the MABAC comprehensive algorithm. Background Art

[0002] Global climate change, frequent storm surges, rapid economic development and continuous population expansion have all brought great pressure to the coastal ecological environment. A good coastal ecological environment is the basis for human survival and sustainable development. Therefore, a comprehensive understanding and evaluation of coastal erosion intensity is of great significance for the sustainable development of the coastal zone.

[0003] Currently, there are many evaluation methods for coastal erosion, including different types such as erosion vulnerability, erosion risk, erosion safety, and erosion intensity. For example: index synthesis method, fuzzy comprehensive evaluation method, fuzzy cloud model, and Gaussian mixture cloud model. Different evaluation methods have their own advantages and disadvantages. The index synthesis method has a simple calculation process and relies on the selection of evaluation indicators and the reasonable grading within the evaluation indicators. Common index synthesis methods include: CVI index and H index, but they only consider natural factors and ignore social factors. The PSR model, VSD model, and SRP model are all conceptual models of evaluation index systems widely used in the study of coastal erosion vulnerability. They all obtain the membership degree of a certain evaluation object by multiplying the weight of each evaluation indicator by the value of the evaluation indicator. Although the exact value of the membership degree of the evaluation object can be effectively calculated and the vulnerability level of the evaluation object can be accurately judged, the most core issues in probability and statistics are ignored, that is, the sample fuzziness has a dependence on randomness, that is, the membership degree is not an exact value. Due to the uniqueness of its evaluation result (membership degree), it is not applicable to coastal erosion evaluation, and the physical meaning of coastal erosion intensity is uncertainty. The cloud model and Gaussian mixture cloud model have been widely used in the problem of comprehensive evaluation in recent years. Their essence is still fuzzy comprehensive evaluation. They reflect the fuzziness of the objective world in the form of clouds, but face the problem of whether the grading within the indicators is reasonable or not. In addition, whether the parameter settings of the model are reasonable or not will also affect the final evaluation result.

[0004] The index weight reflects the relative importance among indices and represents the contribution degree of an index to the overall goal. Whether the determination of the index weight is reasonable is related to the credibility of the comprehensive evaluation result. Therefore, the determination of the weight coefficient should be particularly cautious. Commonly used methods for determining subjective weights include: Delphi method, Analytic Hierarchy Process (AHP), Network Analytic Hierarchy Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The Delphi method can avoid the herd behavior that may occur during face-to-face communication with experts, and the evaluation result is more detailed and in-depth. However, it requires a large amount of time and cost. The AHP decomposes the factors related to the evaluation into multiple levels and conducts qualitative and quantitative analysis on this basis. However, it requires a consistency test and does not consider the correlation among the indices at the same level. The ANP is developed on the basis of the AHP. It considers the correlation among the indices at the same level. However, due to its complex calculation process, its application is difficult. The DEMATEL method fully considers the mutual influence relationship among the indices during the calculation of the subjective weight. However, it is only applicable to the situation where there is a mutual influence relationship among the indices. The objective weight determines the weight coefficient of the evaluation index based on the objective information reflected by the evaluation index, avoiding the influence of subjective factors. Common methods for determining objective weights include: Entropy Weight Method, CRITIC method, and Coefficient of Variation method. The Entropy Weight Method and the Coefficient of Variation method can objectively assign weights according to the information carried by each index, avoiding the uncertainty of the subjective weight. However, they do not consider the correlation among the indices. The CRITIC method considers not only the variability of the index but also the correlation among the indices during the determination of the objective weight, making the determination of the objective weight more reliable and reasonable. A reasonable weight should make full use of the internal law of objective information and expert experience.

[0005] To sum up, there are some deficiencies in the existing methods for evaluating coastal erosion intensity. First, the index weights are simply assigned subjectively or objectively, without fully integrating the advantages of subjective weights and objective weights. Second, the uniqueness of the evaluation result seems accurate, but in fact, it violates the uncertainty and ambiguity of the physical meaning of a specific evaluation object, resulting in a biased evaluation result. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for evaluating coastal erosion intensity based on the MABAC comprehensive algorithm, which can ensure the rationality and credibility of the evaluation result and can intuitively and effectively display the evaluation result, aiming at the above-mentioned problems existing in the prior art.

[0007] The present invention includes the following steps:

[0008] 1) Standardize the data (original sample data) corresponding to the coastal section to be evaluated according to the positive and negative correlations of the evaluation index normalization, and standardize the data of the evaluation unit;

[0009] 2) Distribute the influence comparison judgment form of evaluation index factors to multiple experts in the field, and convert the valid judgment form into the form of a factor matrix, which is the expert scoring matrix;

[0010] 3) Conduct a consistency test on the expert scoring matrix to verify the consistency and rationality of the experts' subjective experience;

[0011] 4) Use the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to calculate the comprehensive relationship matrix between evaluation indicators, and use the comprehensive relationship matrix as the input of the Analytic Network Process (ANP) to calculate the subjective weights of evaluation indicators;

[0012] 5) Calculate the centrality and reasonability of each evaluation indicator through the DEMATEL method, and draw the corresponding scatter plot;

[0013] 6) Calculate the objective weights of evaluation indicators using the CRITIC method, and optimize the distribution of subjective weights and objective weights of evaluation indicators by using the combination method of maximizing the range;

[0014] 7) Use the improved MABAC method to conduct a comprehensive evaluation of coastal erosion intensity to obtain the comprehensive evaluation results of each evaluation unit;

[0015] 8) Sort the comprehensive evaluation results of each evaluation unit, divide the coastal erosion intensity levels, and conduct visual mapping.

[0016] In step 1), the specific steps for normalizing the original data of the evaluation unit are as follows:

[0017] (1) Divide the coast of the Chinese mainland and Hainan Island from north to south into several structural geology and coastal geomorphology sections with different characteristics according to certain rules as the evaluation units of coastal erosion intensity; according to the evaluation factors of coastal erosion intensity recommended by IPCC (2017), combined with the characteristics of China's coastal zone, select several relatively important index factors applicable to the evaluation of coastal erosion intensity in China;

[0018] (2) Set the positive and negative correlations of index factor normalization, including negative correlation normalization, positive correlation normalization and middle normalization, and realize the normalization of the original data to be evaluated.

[0019] In step 2), the expert scoring matrix is transformed from the influence comparison judgment form between two-by-two evaluation indicators given by relevant experts in the field; in the process of expert scoring, the triangular fuzzy number is introduced to solve the fuzziness and uncertainty generated in the process of expert subjective scoring, making the comprehensive evaluation result more objective and reasonable.

[0020] In step 4), the decision-making trial and evaluation laboratory (DEMATEL) method is adopted, combined with the analytic network process (ANP) to calculate the subjective weights of evaluation indicators: First, the expert scoring table is used as the input of the DEMATEL method to calculate the comprehensive relationship matrix of evaluation indicators. This comprehensive relationship matrix not only reflects the direct influence relationship between evaluation indicators but also reflects the indirect influence relationship between evaluation indicators. The row sum of the comprehensive relationship matrix is the influence degree of this evaluation indicator, the column sum is the influenced degree of this evaluation indicator, the centrality is the sum of the influence degree and the influenced degree, and the reason degree is the difference between the influence degree and the influenced degree. The centrality reflects the close relationship between this evaluation indicator and other evaluation indicators, and the reason degree reflects the influence degree of this evaluation indicator on other evaluation indicators. Through the comprehensive relationship matrix, the centrality and reason degree of each evaluation indicator can be calculated, and a scatter plot can be drawn to more intuitively analyze the preference degree of experts for each evaluation indicator. Second, the comprehensive influence relationship matrix is used as the input of the ANP to calculate the subjective weights of each evaluation indicator. The expert scoring table is transformed from the judgments of the influence between pairwise evaluation indicators given by experts in the field. Introducing the expert scoring table not only considers the objective laws existing in things but also introduces subjective cognition. The expert scoring table integrates the opinions of multiple experts, thus avoiding the problem of possible cognitive biases of a single expert.

[0021] In step 5), the corresponding scatter plot is drawn to intuitively identify the preference degree of experts for evaluation indicators.

[0022] In step 6), the CRITIC method is adopted to calculate the objective weights of evaluation indicators. The variability and conflictivity of each evaluation indicator are calculated respectively. Among them, the variability represents the amount of information carried by this evaluation indicator itself, which is represented by variance, and the conflictivity represents the correlation size with other evaluation indicators. In the process of calculating the objective weights of evaluation indicators, the greater the variability and conflictivity, the more weight will be assigned to this evaluation indicator.

[0023] In step 7), the improved MABAC method introduces the BM operator and considers the short-board effect. Among them, the MABAC method is a stable and efficient multi-index comprehensive evaluation method. The BM operator solves the inherent dependence relationship between evaluation indicators. Considering the short-board effect enables experts to more flexibly evaluate the coastal erosion intensity. The improved MABAC method solves the problem of insufficient fuzziness existing in the MABAC method itself. It not only has a simple mathematical logic but also is less affected by the change of index weights, and is more stable and efficient.

[0024] The specific steps of using the improved MABAC method to conduct a comprehensive evaluation of coastal erosion intensity and obtain the comprehensive evaluation results of each evaluation unit include:

[0025] 1) Use the MABAC method to calculate the approximate region matrix of evaluation indicators;

[0026] 2) Calculate the distance matrix by comparing each evaluation unit with the approximate region matrix;

[0027] 3) Aggregate the distance matrix using the BM operator to obtain the group benefits of each evaluation unit;

[0028] 4) Calculate the individual regrets of each evaluation unit;

[0029] 5) Standardize and weight the group benefits and individual regrets of each evaluation unit to obtain the comprehensive evaluation results of each evaluation unit.

[0030] Compared with the prior art, the present invention has the following outstanding advantages:

[0031] 1) By introducing the combination of triangular fuzzy numbers and subjective expert scoring, the ambiguity and uncertainty generated in the subjective expert scoring process are solved, making the evaluation results more objective and reasonable.

[0032] 2) By combining the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and the Analytic Network Process (ANP), the subjective weights of each evaluation index are calculated. This not only solves the shortcomings of these two methods themselves, but also allows for the drawing of a scatter plot to intuitively analyze the preference degree of experts for each evaluation index while obtaining the subjective weights of the evaluation indexes.

[0033] 3) Use the combination method of maximizing the range to combine the subjective weights and objective weights of the evaluation indexes. Compared with the traditional addition combination method and multiplication combination method, this method takes maximizing the discrimination degree between evaluation objects as the objective function, has better interpretability, and the combination result is more reasonable.

[0034] 4) Use the improved MABAC method to comprehensively evaluate the coastal erosion intensity. This method is a stable and efficient multi-index comprehensive evaluation method. The present invention solves the deficiencies existing in this method by introducing the BM operator. The MABAC method is only applicable to independent evaluation indexes, but in the research process of coastal erosion intensity, there are often interdependent relationships between evaluation indexes. Therefore, the BM operator can well solve the inherent dependence relationship between evaluation indexes. At the same time, considering the short-board effect, it allows experts to evaluate the coastal erosion intensity from multiple perspectives.

[0035] 5) Sort and visualize based on the comprehensive score results of the evaluated objects, so as to intuitively and effectively display the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic flow chart of the present invention.

[0037] Figure 2It is a visualization graph of the comprehensive evaluation results of the coastal areas of mainland China and Hainan Island in the 1980s.

[0038] Figure 3 It is a visualization graph of the comprehensive evaluation results of the coastal areas of mainland China and Hainan Island in the 2010s.

[0039] Figure 4 It is a scatter plot of the centrality and reasonability of each evaluation index. Specific implementation manner

[0040] The following combines the attached drawings, tables and formulas to illustrate the implementation manner of the present invention.

[0041] The present invention proposes a method for evaluating the coastal erosion intensity based on the MABAC comprehensive algorithm to evaluate the coastal erosion intensity of each section of the mainland and Hainan Island. According to the positive and negative correlations of the evaluation index standardization, the data of the evaluation unit is standardized; a comparison judgment form of the influence of evaluation index factors is distributed to multiple experts in the field, and the valid judgment form is converted into a factor matrix form, and the consistency of the factor matrix is ​​tested to verify the consistency of the subjective experience of the experts; the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is adopted, combined with the Analytic Network Process (ANP) to calculate the subjective weight of the evaluation index, and the CRITIC method is used to calculate the objective weight of the evaluation index. The subjective weight and objective weight of the evaluation index are optimized by the combination method of maximizing the range; then, the improved MABAC method considering the short board effect by introducing the BM operator is used for the comprehensive evaluation of the coastal erosion intensity to obtain the comprehensive evaluation results of each evaluation unit; the comprehensive evaluation results are sorted and graded, and Python is used to visually display each evaluation unit.

[0042] For the data of the evaluation unit, the coast of the coastal areas of mainland China and Hainan Island can be divided into several (36 evaluation units in this experiment) different characteristic tectonic geology and coastal geomorphology sections from north to south according to certain rules as the evaluation units of the coastal erosion intensity; according to the coastal erosion intensity evaluation factors recommended by the Intergovernmental Panel on Climate Change (IPCC) in 2017, combined with the characteristics of China's coastal zone, several (10 in this experiment) relatively important index factors applicable to the evaluation of China's coastal erosion intensity are selected; the positive and negative correlations of the index factor standardization are set, divided into negative correlation standardization, positive correlation standardization and middle standardization, and the original data to be evaluated is standardized.

[0043] Figure 1 The flow chart of the evaluation system of the present invention is given. The method of the embodiment of the present invention can be divided into 5 stages, specifically including:

[0044] (1) Determination of the subjective weights of evaluation indicators. This stage mainly includes the determination of the expert scoring table, the calculation of the subjective weights of evaluation indicators, and the drawing of the corresponding scatter plots.

[0045] 1) Determine the fuzzy scale of the evaluation indicators in the subjective weight method. Table 1 uses triangular fuzzy numbers to express the specific language meaning in real life, which is more objective and reasonable.

[0046] Table 1 Classification of coastal erosion intensity

[0047]

[0048] 2) Collect expert opinions and calculate the average matrix First, we establish the direct relationship fuzzy matrix according to Table 1 Where n represents the number of evaluation indicators, Indicates the evaluation index C i Evaluation index C j Invite k experts to evaluate the evaluation indicators, and each expert can obtain an n×n non-negative matrix Then, the opinions of k experts are synthesized using formula (1) to obtain the direct relationship fuzzy matrix.

[0049]

[0050] 3) Calculate the standardized direct relationship fuzzy matrix

[0051]

[0052] 4) Calculate the comprehensive relationship fuzzy matrix During the calculation process, the normalization can directly affect the matrix Split into sub-matrices (D1, D2, D3).

[0053]

[0054] 5) Calculate the impact of indicators Influence Cause degree and centrality The influence degree is the degree to which the evaluation indicator affects other indicators. The influence degree is the degree to which the indicator is affected by other indicators. The greater the centrality, the closer the relationship between the indicator and other indicators, and the greater the causal degree, the greater the influence of the indicator on other indicators. Figure 4 The centrality and causality scatter plots of each evaluation index are given.

[0055]

[0056] 6) Construct the weighted supermatrix \(W\). Defuzzify the comprehensive relation fuzzy matrix through formula (9) and construct the weighted supermatrix using formula (10).

[0057] \(t=(t (l) + 4\cdot t (m) + t(r))\cdot6 -1 Formula (9)

[0058]

[0059] 7) Solve the limit supermatrix. After calculation, any column of the matrix is taken as the subjective weight of the index. Thus, the steps of the subjective weight method end, and the subjective weight of the index is obtained.

[0060]

[0061] (2) Determination of the objective weight of the evaluation index. In this stage, the CRITIC method is used to calculate the objective weight of the evaluation index. This method assigns corresponding weights to each evaluation index by calculating the difference and conflict of each evaluation index.

[0062] 1) Standardize the original data matrix \(X = [x ij \). For positive indicators, formula (12) is used for standardization, and for negative indicators, formula (13) is used for standardization.

[0063]

[0064] 2) Calculate the variability \(S j \) of each index.

[0065]

[0066] 3) Calculate the conflict \(R j \) of each index.

[0067]

[0068] Among them, \(COV(i,j)\) represents the covariance of evaluation index \(i\) and evaluation index \(j\), \(\sigma i ,\sigma j \) respectively represent the standard deviations of the two evaluation indexes, and \(r ij \) indicates the correlation coefficient between evaluation indexes \(i\) and \(j\).

[0069] 4) Calculate the objective weight of each index. Calculate the information amount of each index through formula (17). The index with a larger information amount should be assigned more weight, and the index with a smaller information amount should be assigned less weight. Calculate the objective weight of each index through formula (18).

[0070] C j=S j ×R j Formula (17)

[0071]

[0072] (3) Determination of the subjective weight and objective weight of evaluation indicators. The subjective weight and objective weight of evaluation indicators are optimized and allocated by using the combination method of maximizing the range. The combination method of maximizing the range takes the subjective weight and objective weight as the value range of the weight coefficient of the evaluation indicator, takes the maximization of the variance of the evaluation unit as the objective function, and obtains the final weight of each evaluation indicator by solving. This weight combines the advantages of subjective and objective weights, is more reasonable, and the interpretability of this method is stronger.

[0073] 1) Combine subjective weight and objective weight. First, construct a reasonable weight value range for the indicators, that is, jointly form a reasonable weight value range with the subjective weight and objective weight of this indicator.

[0074]

[0075] 2) In order to effectively distinguish the evaluated objects, take the maximization of the variance of the evaluated objects under the combined weight as the objective function. Construct the following goal programming:

[0076]

[0077] Among them, m represents the number of evaluated objects, and n represents the number of evaluation indicators.

[0078] (4) Comprehensive evaluation of coastal erosion intensity. In this stage, the improved MABAC method is used to comprehensively evaluate the coastal erosion intensity. The MABAC method has simple mathematical logic, is stable and efficient, and is more suitable for evaluating coastal erosion intensity compared with other methods. Aiming at the deficiencies of the MABAC method itself, the present invention solves the inherent dependence relationship between evaluation indicators by introducing the BM operator, and makes the comprehensive evaluation process more flexible by considering the short-board effect.

[0079] 1) Construct an evaluation matrix X = (x ij ) m×n . Among them, x ij is the value of the evaluated object a i on the evaluation indicator c j . m represents the number of evaluated objects, and n is the number of evaluation indicators.

[0080] 2) Calculate the standardized matrix N = (n ij ) m×n . Among them, the positive indicators are standardized by formula (12), and the reverse indicators are standardized by formula (13).

[0081] 3) Calculate the weight matrix V = (v ij ) m×n .

[0082] v ij =w i ·(n ij +1) Formula (21)

[0083] 4) Determine the boundary approximation region matrix (G).

[0084]

[0085] 5) Calculate the distance matrix Q = (q ij ) m×n .

[0086]

[0087] 6) Distance matrix Q = (q ij ) m×n Non-negative normalization. The BM operator can only aggregate non-negative numbers, so the distance matrix needs to be non-negatively normalized.

[0088]

[0089] 7) Calculate group benefit s i Group benefit refers to the score obtained by aggregating each evaluation unit through the BM operator, which represents the comprehensive level of the evaluation unit.

[0090]

[0091] Where p and q are variable parameters, p, q ≥ 0, but p and q cannot be 0 at the same time.

[0092] 8) Calculate individual regret r i Individual regret represents the lowest-scoring indicator in the evaluation unit.

[0093]

[0094] 9) Standardized group benefits i and individual regret i .

[0095]

[0096] 10) Calculate the comprehensive scores of the evaluation units and rank them.

[0097] Q i =v·NS i +(1-v)·NR i Formula (28)

[0098] Among them, v is a weighting coefficient determined by experts according to experience.

[0099] (5) Sort and classify the results of the comprehensive evaluation, and conduct visual display. Figure 2 Give the visualization graph of the comprehensive evaluation results of the coastal areas of mainland China and Hainan Island in the 1980s. Figure 3 Give the visualization graph of the comprehensive evaluation results of the coastal areas of mainland China and Hainan Island in the 2010s.

[0100] Regarding the evaluation of coastal erosion intensity, the present invention focuses on making up for the subjective dependence between evaluation index factors, increasing the flexibility of the expert judgment process, and innovating a comprehensive evaluation method for coastal erosion intensity. The method of the present invention can intuitively and effectively display the evaluation results, and the results are reasonable and credible.

[0101] The above embodiments are only preferred embodiments of the present invention and should not be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A method for evaluating the intensity of coastal erosion based on the MABAC comprehensive algorithm, characterized in that It includes the following steps: 1) Standardize the original sample data corresponding to the coastal segments to be evaluated and the data of the evaluation units according to the positive and negative correlations of the evaluation index standardization; 2) Invite and distribute the influence comparison judgment tables of evaluation index factors to multiple experts in the field, and convert the valid judgment tables into the form of a factor matrix, which is the expert scoring matrix; 3) Conduct a consistency test on the expert scoring matrix to verify the consistency and rationality of the subjective experience of experts; 4) Use the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to calculate the comprehensive relationship matrix between evaluation indexes, and use the comprehensive relationship matrix as the input of the Analytic Network Process (ANP) to calculate the subjective weights of evaluation indexes; 5) Calculate the centrality and reasonability of each evaluation index by the DEMATEL method, and draw the corresponding scatter plot; 6) Calculate the objective weights of evaluation indexes by the CRITIC method, and optimize the allocation of subjective weights and objective weights of evaluation indexes by using the combination method of maximizing the range; 7) Use the improved MABAC method to conduct a comprehensive evaluation of coastal erosion intensity and obtain the comprehensive evaluation results of each evaluation unit; In the improved MABAC method, the BM operator and the consideration of the short board effect are introduced. Among them, the MABAC method is a stable and efficient multi-index comprehensive evaluation method. The BM operator solves the inherent dependence relationship between evaluation indexes, and considering the short board effect enables experts to evaluate coastal erosion intensity more flexibly; the improved MABAC method solves the ambiguity deficiency existing in the MABAC method itself, is not only simple in mathematical logic, but also less affected by the change of index weights, and is more stable and efficient; The specific steps of using the improved MABAC method to conduct a comprehensive evaluation of coastal erosion intensity and obtain the comprehensive evaluation results of each evaluation unit include: 7.1 Use the MABAC method to calculate the approximate area matrix of evaluation indexes; 7.2 Calculate the distance matrix by comparing each evaluation unit with the approximate area matrix; 7.3 Aggregate the distance matrix by using the BM operator to obtain the group benefits of each evaluation unit; 7.4 Calculate the individual regret of each evaluation unit, and the individual regret represents the index with the lowest score in the evaluation unit; 7.5 Standardize and weight the group benefits and individual regrets of each evaluation unit to obtain the comprehensive evaluation results of each evaluation unit; 8) Sort the comprehensive evaluation results of each evaluation unit, divide the coastal erosion intensity levels, and conduct visual mapping.

2. The coastal erosion intensity evaluation method based on the MABAC comprehensive algorithm according to claim 1, characterized in that In step 1), the specific steps of standardizing the data of the evaluation unit are: (1) Divide the coast of the coastal area into several structural geology and coastal geomorphology sections with different characteristics from north to south according to certain rules as the evaluation units of coastal erosion intensity; according to the coastal erosion intensity evaluation factors recommended by IPCC (2017), combined with the characteristics of the coastal zone, select several relatively important index factors applicable to coastal erosion intensity evaluation; (2) Set the positive and negative correlations of index factor standardization, which are divided into negative correlation standardization, positive correlation standardization and centered standardization, and realize the standardization of the original data to be evaluated.

3. The coastal erosion intensity evaluation method based on the MABAC comprehensive algorithm according to claim 1, characterized in that In step 2), the expert scoring matrix is transformed from the pairwise influence comparison judgment table of evaluation indicators given by relevant experts in the field. During the process of expert scoring, triangular fuzzy numbers are introduced to solve the ambiguity and uncertainty generated in the subjective scoring process of experts, making the comprehensive evaluation result more objective and reasonable.

4. The coastal erosion intensity evaluation method based on the MABAC comprehensive algorithm according to claim 1, characterized in that In step 4), the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is adopted, combined with the Analytic Network Process (ANP) to calculate the subjective weights of evaluation indicators. First, the expert scoring table is used as the input of the DEMATEL method, and the comprehensive relationship matrix of evaluation indicators is calculated. This comprehensive relationship matrix not only reflects the direct influence relationship between evaluation indicators but also reflects the indirect influence relationship between evaluation indicators. The row sum of the comprehensive relationship matrix is the influence degree of this evaluation indicator, the column sum is the influenced degree of this evaluation indicator, the centrality is the sum of the influence degree and the influenced degree, and the reason degree is the difference between the influence degree and the influenced degree. The centrality reflects the close relationship between this evaluation indicator and other evaluation indicators, and the reason degree reflects the influence degree of this evaluation indicator on other evaluation indicators. Through the comprehensive relationship matrix, the centrality and reason degree of each evaluation indicator are calculated, and a scatter plot is drawn to more intuitively analyze the preference degree of experts for each evaluation indicator. Secondly, the comprehensive influence relationship matrix is used as the input of the ANP to calculate the subjective weights of each evaluation indicator. The expert scoring table is transformed from the pairwise influence judgment of evaluation indicators given by experts in the field. Introducing the expert scoring table not only considers the objective laws existing in things but also introduces subjective understanding. The expert scoring table integrates the opinions of multiple experts, thus avoiding the problem of possible cognitive biases of a single expert.

5. The method for evaluating coastal erosion intensity based on the MABAC comprehensive algorithm according to claim 1, wherein In step 5), the corresponding scatter plot is drawn to visually identify the preference degree of experts for evaluation indicators.

6. The coastal erosion intensity evaluation method based on the MABAC comprehensive algorithm according to claim 1, wherein In step 6), the CRITIC method is adopted to calculate the objective weights of evaluation indicators, and the variability and conflictivity of each evaluation indicator are calculated respectively. Among them, the variability represents the amount of information carried by this evaluation indicator itself, which is represented by variance, and the conflictivity represents the correlation size with other evaluation indicators. During the process of calculating the objective weights of evaluation indicators, the greater the variability and conflictivity, the more weight will be assigned to this evaluation indicator.

7. The coastal erosion intensity evaluation method based on the MABAC comprehensive algorithm according to claim 1, wherein In step 8), visual plotting is carried out, and Python is used to visually display each evaluation unit.

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