A wetland degradation risk assessment method and system

Abstract

The present invention discloses a wetland degradation risk assessment method and system, specifically relates to a method for wetland degradation risk grading using a risk assessment method coupled with an analytic hierarchy process (AHP), an entropy weight method (EWM) and an improved catastrophe theory (ICT), comprising: (1) establishing an alternative set of evaluation indicators: selecting human factors, natural factors and socioeconomic factors that affect wetland degradation in an evaluation area, and selecting characterization factors of wetland degradation from three aspects: wetland structure, wetland state and wetland function; (2) using a variance inflation factor (VIF) to screen relatively independent unit evaluation indicators; (3) ranking the importance of evaluation indicators based on the comprehensive weights determined by AHP and EWM; (4) using ICT to calculate a risk assessment value of wetland degradation; and (5) comparing the risk assessment value with a risk level threshold to determine the risk level of wetland degradation.

Description

Technical Field

[0001] The present invention is a wetland degradation risk assessment method and system based on the coupling of analytic hierarchy process (AHP), entropy weight method (EWM), and improved catastrophe theory (ICT). Specifically, it is a method for determining the wetland degradation risk level using a risk assessment method, and belongs to the technical field of ecological risk assessment. Background Art

[0002] Wetland ecosystems are rich and diverse, providing functions such as regulating runoff, improving water quality, and replenishing groundwater resources. They are crucial to human survival. However, due to the characteristics of their surrounding environments and interference from human activities, wetland ecosystems are also highly fragile. In addition to surface water, groundwater is also a significant source of wetland water resources. However, in recent decades, to meet growing water demands, human exploitation of groundwater has steadily increased, leading to serious over-exploitation. In particular, declining groundwater levels around wetland environments have effectively competed with the wetlands for water, exacerbating infiltration, leading to a continuous decline in wetland area and increasingly severe degradation of wetland systems, significantly hindering regional sustainable development. Studies on wetland degradation have often analyzed wetland degradation based on changes in wetland landscape pattern indices, analysis of wetland landscape pattern evolution, and changes in wetland surface water levels. However, research examining degradation risks is limited, and most methods employed rely on traditional analytic hierarchy process (AHP) and comprehensive index methods. With the advancement of ecological civilization, quantitative and accurate risk assessment of wetland degradation is crucial for the protection, restoration, research, and management of wetland ecosystems.

[0003] The choice of risk assessment method is a key factor influencing risk assessment results. Methods that assess the impact of an assessment object through subjective judgment indicators are highly arbitrary, including the analytic hierarchy process (AHP) and fuzzy evaluation methods. The entropy weight method assigns weights based on the information entropy contained in the data and does not involve subjective judgment, making it an objective weighting method. In recent years, the CT method, as an objective risk assessment method, has been widely used in multi-indicator evaluation studies, such as groundwater vulnerability assessment, water resource sustainable utilization assessment, and mining area ecological and geological environmental safety assessment. Because CT requires consideration of the relative importance of indicators, some researchers have combined CT with AHP to study groundwater potential zones and flood risk analysis. However, previous studies have rarely applied CT to wetland degradation risk assessment.

[0004] Therefore, it is necessary to explore a risk assessment method to provide new research ideas for wetland degradation risk classification. Summary of the Invention

[0005] The present invention proposes a wetland degradation risk assessment method to solve the above problems. By selecting external factors affecting wetland degradation and internal characterization factors of wetland degradation, using AHP and EWM to rank the importance of evaluation indicators, and improving CT, it has solved some defects in CT evaluation and provided a new research idea for wetland degradation risk assessment.

[0006] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is:

[0007] First, a wetland degradation risk assessment method is provided, including:

[0008] Step 1: Establish an alternative set of evaluation indicators;

[0009] Step 2: Based on the Variance Inflation Factor (VIF) value of the evaluation indicator, independent evaluation indicators are selected from the evaluation indicator candidate set through trial and error method;

[0010] Step 3: Use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct the comprehensive weight and calculate the comprehensive weight of the independent evaluation indicators selected;

[0011] Step 4: Sort the evaluation indicators according to the size of the comprehensive weights, and construct a multi-level risk evaluation indicator system based on the ranking of the evaluation indicators;

[0012] Step 5: Use the min-max standardization method to standardize the original evaluation index data in the risk assessment index system;

[0013] Step 6: Select the corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system and perform recursive calculation to obtain the total mutation membership value;

[0014] Step 7: converting the total mutation membership value into a risk assessment value using a power function fitting method;

[0015] Step 8: Compare the risk assessment value with the risk level threshold, and determine the wetland degradation risk level based on the comparison result.

[0016] In some embodiments, step 1, establishing a candidate set of evaluation indicators, includes: selecting human factors, natural factors, and socioeconomic factors that affect wetland degradation in the area to be evaluated, and forming a candidate set of evaluation indicators together with wetland degradation characterization factors.

[0017] In some embodiments, step 2 includes: selecting evaluation indicators with a VIF not greater than 10 from the candidate set of evaluation indicators as independent evaluation indicators.

[0018] Furthermore, evaluation indicators with a VIF of no more than 5 were screened as independent evaluation indicators.

[0019] In some embodiments, the step 3 uses the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct a comprehensive weight, including:

[0020]

[0021] Among them, W i is the comprehensive weight of the i-th indicator, α i is the weight of the i-th indicator calculated by AHP, β i The weight of the i-th indicator calculated for EWM. The larger the comprehensive weight, the more important the indicator.

[0022] In some embodiments, in step 4, a multi-level risk assessment indicator system is constructed based on the ranking of the assessment indicators, including:

[0023] The external pressure factors faced by wetlands are divided into a "stress degree" system, and the internal characterization factors of wetland degradation are divided into a "degradation degree" system; an evaluation index system consisting of a target layer, a criterion layer and an indicator layer is constructed according to the principle that the greater the comprehensive weight of the indicator, the higher the indicator ranking; the target layer is the wetland degradation risk assessment value, and the criterion layer is the stress degree and degradation degree.

[0024] In some embodiments, in step 5, the standardization method comprises:

[0025] For positive indicators, the standardized formula is:

[0026]

[0027] Among them, x i ′ is the normalized value of the ith data of the positive indicator x; x i is the i-th original value of the positive indicator x; x max is the maximum value of the positive indicator x; min is the minimum value of the positive indicator x;

[0028] For negative indicators, the standardized formula is:

[0029]

[0030] Among them, y i ′ is the normalized value of the i-th data of the negative indicator y; y i is the i-th original value of the negative indicator y; y max is the maximum value of the negative indicator y; min is the minimum value of the negative indicator y.

[0031] In some embodiments, in step 6, a normalized formula of the mutation theory CT is used to perform a recursive operation to obtain a total mutation membership value;

[0032] Catastrophe theory (CT) divides system variables into state variables and control variables. State variables reflect the system's behavior, i.e., the target of evaluation. Control variables are external factors affecting the system's operation, i.e., evaluation indicators. Commonly used catastrophe types are those with a one-dimensional state variable and no more than four-dimensional control variables, including wrinkles, cusps, swallowtails, and butterflies.

[0033] The mutation type is wrinkle: the state variable is 1-dimensional, the control variable is 1-dimensional, and the potential function is f x =x 3 +ax, the normalization formula uses The schematic diagram is

[0034] The mutation type is cusp: the state variable is 1-dimensional, the control variable is 2-dimensional, and the potential function is f x =x 4 +ax 2 +bx, the normalization formula is The schematic diagram is

[0035] The mutation type is swallowtail: the state variable is 1-dimensional, the control variable is 3-dimensional, and the potential function is f x =x 5 +ax 3 +bx 2 +cx, the normalization formula is The schematic diagram is

[0036] The mutation type is butterfly: the state variable is 1-dimensional, the control variable is 4-dimensional, and the potential function is f x =x 6 +ax 4 +bx 3 +cx 2 +dx, the normalization formula uses The schematic diagram is

[0037] When the number of mutation type control variables exceeds 4, the rules of the potential functions of the fold, cusp, swallowtail and butterfly mutation types are summarized, and the mutation type potential function f with n-dimensional control variables is deduced. x :

[0038] f x =x n+2 +a1x n +a2x n-1 +a3x n-2 +…+a n x

[0039] Among them, f x is the potential function; x represents the state variable; n is the number of control variables; a i (i=1,2,3…n) represents n-dimensional control variables;

[0040] Summarizing the rules of calculating normalized formulas for potential functions of fold, cusp, swallowtail and butterfly mutation types, we can deduce the normalized formula containing n-dimensional control variables:

[0041]

[0042] Among them, x i (i=1,2,3…n) is the membership value of the n-dimensional control variable;

[0043] In the recursive operation process, if the control variables (1, 2, 3, ..., n) of the subsystem can complement each other, then the mutation membership value of the control variables of the upper layer system tends to the average value x = (x1 + x2 + x2 + ... + x n ) / n; If the control variables (1,2,3,…,n) of the subsystem cannot complement each other, the mutation membership value of the control variable of the upper layer system takes the minimum value, that is, x=min{x1,x2,x3,…,x n}.

[0044] In some embodiments, step 7, converting the total mutation membership value into a risk assessment value using a power function fitting method, comprises:

[0045] Take the standardized values ​​of all underlying indicators as x i (i=1,2,…,n), determine the mutation type of each subsystem according to the established evaluation index system, and recursively calculate the total mutation membership value y i (ii=1,2,…,n); when n is large enough, establish the total mutation membership value y i The normalized value x of the underlying indicator i The power function relationship; by determining the coefficient R 2 Determine the degree of curve fitting, coefficient R 2 The closer the value is to 1, the better the fit;

[0046] According to the obtained power function relationship, the total mutation membership value y i Converted into a unified risk assessment value y′ i .

[0047] Secondly, a wetland degradation risk assessment system is provided, comprising:

[0048] The indicator candidate set building module is configured to: establish an evaluation indicator candidate set;

[0049] The indicator screening module is configured to: screen out independent evaluation indicators from the evaluation indicator candidate set through trial and error method according to the variance inflation coefficient (VIF) value of the evaluation indicator;

[0050] The comprehensive weight calculation module is configured to: use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct the comprehensive weight, and calculate the comprehensive weight of the selected independent evaluation indicators;

[0051] The system construction module is configured to: sort the evaluation indicators according to the size of the comprehensive weight, and construct a multi-level risk evaluation indicator system according to the sorting of the evaluation indicators;

[0052] The standardization processing module is configured to: standardize the original evaluation index data in the risk evaluation index system using the min-max standardization method;

[0053] The recursive operation module is configured to: select a corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system, perform recursive operation, and obtain a total mutation membership value;

[0054] A power function fitting module is configured to: convert the total mutation membership value into a risk assessment value using a power function fitting method;

[0055] The risk level determination module is configured to compare the risk assessment value with the risk level threshold and determine the wetland degradation risk level according to the comparison result.

[0056] Beneficial Effects: The method of the present invention integrates AHP and EWM into the evaluation process, addressing the shortcomings of the catastrophe theory (CT) evaluation results that are subject to the ranking of indicator importance. It also uses a power function fitting conversion method to address the shortcomings of the CT calculation results that are centralized and overvalued. Compared with the CT method, the ICT method coupled with AHP and EWM can improve the data resolution level and make the evaluation results more objective and accurate. The method of the present invention can realize the multi-year wetland degradation risk rating assessment and evolution trend analysis, providing a new research approach for the study of wetland degradation risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0058] Figure 1 Flowchart of the method of the present invention.

[0059] Figure 2 Schematic diagram of power function fitting transformation.

[0060] Figure 3 The comparison chart before and after the improvement of power function fitting is shown as an example. DETAILED DESCRIPTION

[0061] The present invention will be further described below with reference to the accompanying drawings and specific examples. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0062] Example 1

[0063] A wetland degradation risk assessment method, comprising:

[0064] Step 1: Establish an alternative set of evaluation indicators;

[0065] Step 2: Based on the Variance Inflation Factor (VIF) value of the evaluation indicator, independent evaluation indicators are selected from the evaluation indicator candidate set through trial and error method;

[0066] Step 3: Use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct the comprehensive weight and calculate the comprehensive weight of the independent evaluation indicators selected;

[0067] Step 4: Sort the evaluation indicators according to the size of the comprehensive weights, and construct a multi-level risk evaluation indicator system based on the ranking of the evaluation indicators;

[0068] Step 5: Use the min-max standardization method to standardize the original evaluation index data in the risk assessment index system;

[0069] Step 6: Select the corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system and perform recursive calculation to obtain the total mutation membership value;

[0070] Step 7: converting the total mutation membership value into a risk assessment value using a power function fitting method;

[0071] Step 8: Compare the risk assessment value with the risk level threshold, and determine the wetland degradation risk level based on the comparison result.

[0072] In some embodiments, as Figure 1 FIG. 1 is a flow chart of a wetland degradation risk assessment method based on AHP-EWM-ICT coupling according to an embodiment of the present invention. The method includes the following steps:

[0073] Step 1: Select the human factors, natural factors and socio-economic factors that affect wetland degradation in the assessment area, and form a set of evaluation indicators together with the wetland degradation characterization factors.

[0074] Step 2: To prevent mutual influence between the evaluation indicators, use Python software to calculate the VIF value of the evaluation indicators. Through trial and error, select independent evaluation indicators from the evaluation indicator candidate set. It is generally believed that when the VIF value is greater than 10 (strictly 5), it indicates that there is a serious collinearity problem between the evaluation indicators.

[0075] Step 3: Use the comprehensive weights obtained by the analytic hierarchy process (AHP) and entropy weight method (EWM) to rank the importance of indicators. The larger the weight, the more important the indicator. Combined weighting can make up for the shortcomings of single weighting and reduce subjective influence. The equation used to calculate the comprehensive weight is:

[0076]

[0077] Among them, W i is the comprehensive weight of the i-th indicator, α i is the weight of the i-th indicator calculated by AHP, β i The weight of the ith indicator calculated for EWM.

[0078] Step 4: By determining the comprehensive weight, a multi-level wetland degradation risk assessment indicator system is constructed according to the principle that the greater the weight, the higher the ranking of the indicators of each subsystem.

[0079] Step 5: Use the min-max normalization method to convert the original evaluation index data into a dimensionless value between [0, 1] to facilitate comparison between indicators. For positive indicators, the larger the index, the better, while for negative indicators, the smaller the index, the better.

[0080] For positive indicators, the standardized formula is:

[0081]

[0082] Where x′ i is the normalized value of the ith data of the positive indicator x; i is the i-th original value of the positive indicator x; x max is the maximum value of the positive indicator x; min is the minimum value of the positive indicator x.

[0083] For negative indicators: the standardized formula is:

[0084]

[0085] Among them, y′ i is the normalized value of the i-th data of the negative indicator y; i is the i-th original value of the negative indicator y; y maxis the maximum value of the negative indicator y; min is the minimum value of the negative indicator y.

[0086] Step 6: Select the corresponding normalization formula according to the number of control variables contained in each layer of the system and perform recursive calculation to obtain the total mutation membership value.

[0087] Common mutation types are those containing state variables and control variables no more than 4 dimensions (Table 1).

[0088] Table 1

[0089]

[0090] When the number of mutation type control variables exceeds 4, the rules of the mutation type potential function in Table 1 are summarized and the mutation type potential function f with n-dimensional control variables is recursively obtained. x :

[0091] f x =x n+2 +a1x n +a2x n-1 +a3x n-2 +…+a n x

[0092] Among them, f x is the potential function; x represents the state variable; n is the number of control variables; a i (i=1,2,3…n) represents n-dimensional control variables.

[0093] Then, by summarizing the existing rules of potential function calculation normalization formula, we can recursively obtain the normalization formula containing n-dimensional control variables:

[0094]

[0095] Among them, x i (i=1,2,3…n) is the membership value of the n-dimensional control variable.

[0096] In the recursive operation process, if the control variables in the subsystem are complementary and can supplement each other, the membership value of the control variables of the upper system tends to the average value x = (x1+x2+x2+…+x n ) / n; If the control variables in the subsystem cannot complement each other, the membership value of the control variable of the upper layer system takes the minimum value, that is, x=min{x1,x2,x3,…,x n}. Repeat this recursive operation until the total mutation membership value is calculated.

[0097] Step 7: The total mutation membership value obtained is converted into an evaluation value with a significant range by power function fitting, and then compared with the risk level threshold to determine the final risk level.

[0098] Take the standardized values ​​of all underlying indicators as x i (i=1,2,…,n), determine the mutation type of each subsystem according to the established evaluation index system, and recursively calculate the total mutation membership value y i (i=1,2,…,n). When n is large enough, the total mutation membership value y can be established i The normalized value x of the underlying indicator i The power function relationship. By determining the coefficient R 2 The degree of fitting of the curve is judged. The closer the value is to 1, the better the degree of fitting is. According to the power function relationship obtained, the total mutation membership value y i Converted into a unified risk assessment value y′ i , which can increase the range of evaluation values ​​and facilitate risk comparison analysis.

[0099] Figure 2 A schematic diagram of the power function fitting transformation is shown.

[0100] Application examples:

[0101] This embodiment selects a certain area as the research area to illustrate the specific implementation process of the present invention:

[0102] Step 1: Establish a candidate set of evaluation indicators. The primary human factor affecting wetland degradation within the study area is groundwater extraction. Natural factors include precipitation, temperature, and other factors. Socioeconomic factors, including demographic and economic factors, indirectly influence human water demand and exacerbate groundwater extraction. Indicators representing wetland degradation were selected from three perspectives: wetland structure, wetland status, and wetland function.

[0103] Step 2: Use the VIF value to select mutually independent evaluation indicators through trial and error. Table 2 shows the 10 mutually independent evaluation indicators selected for this example. Using data from 2000 to 2020 as the raw data for this example, the interannual dynamics of wetland degradation risk levels in a specific region from 2000 to 2020 were analyzed.

[0104] Table 2

[0105]

[0106] Step 3: Use AHP to calculate the subjective weight of the evaluation index, use EWM to calculate the objective weight of the evaluation index, and then calculate the comprehensive weight using the following formula:

[0107]

[0108] Among them, W i is the comprehensive weight of the i-th indicator, α i is the weight of the i-th indicator calculated by AHP, β i The weight of the ith indicator calculated for EWM.

[0109] The comprehensive weight is used as the basis for ranking the importance of evaluation indicators in each subsystem. The larger the weight, the more important the indicator. Table 3 shows the weights calculated by AHP and EWM, as well as the calculated comprehensive weight.

[0110] Table 3

[0111]

[0112] Step 4: External pressure factors facing wetlands are divided into a "stress degree" system, and internal factors indicating wetland degradation are divided into a "degradation degree" system. Based on the principle that the greater the overall weight of an indicator, the higher the ranking of the indicator, an evaluation index system is constructed, consisting of a target layer, a criterion layer, and an indicator layer. The target layer is the wetland degradation risk assessment value, and the criterion layer is the stress degree and degradation degree. Table 4 shows the evaluation index system created in this example.

[0113] Table 4

[0114]

[0115] Note: “+” represents a positive indicator; “-” represents a negative indicator.

[0116] Step 5: Use the min-max normalization method to convert the original evaluation index data into a dimensionless value between [0,1].

[0117] For positive indicators, the standardized formula is:

[0118]

[0119] Where x′ i is the normalized value of the ith data of the positive indicator x; i is the i-th original value of the positive indicator x; x max is the maximum value of the positive indicator x; min is the minimum value of the positive indicator x.

[0120] For negative indicators: the standardized formula is:

[0121]

[0122] Among them, y′ i is the normalized value of the i-th data of the negative indicator y; iis the i-th original value of the negative indicator y; y max is the maximum value of the negative indicator y; min It is the minimum value of the negative indicator y.

[0123] Table 5 shows the standardized values ​​of the 10 independent evaluation indicators selected in this embodiment.

[0124] Table 5

[0125]

[0126]

[0127] Step 6: Select the corresponding normalization formula according to the number of control variables contained in each layer system and perform recursive operation to obtain the total mutation membership value. This embodiment includes a total of three subsystems, namely subsystem 1 composed of A, B1 and B2; subsystem 2 composed of B1 and corresponding evaluation indicators; subsystem 3 composed of B2 and corresponding evaluation indicators. Because the target layer A contains 2 control variables (B1 and B2), the "cusp" type normalization formula in Table 1 is directly used for calculation. B1 contains 6 evaluation indicators. Through the normalization formula of n-dimensional control variables, the normalization calculation formula with 6-dimensional control variables is obtained:

[0128]

[0129] B2 contains four evaluation indicators, which are directly calculated using the "butterfly" type normalization formula in Table 1.

[0130] In this embodiment, the three subsystem control variables complement each other, so the mutation membership value calculation formula corresponding to the "complementary" rule is used to recursively obtain the total mutation membership value. Specifically, the mutation membership value x of the stress degree B1 is B1 The calculation formula is:

[0131]

[0132] The mutation membership value x of the degradation degree B2 B2 The calculation formula is:

[0133]

[0134] Total mutation membership value x A The calculation formula is:

[0135]

[0136] Step 7: The total mutation membership value obtained is converted into an evaluation value with a significant range by fitting a power function, and then compared with the risk level threshold to determine the final risk level. This example uses the equal interval method to classify wetland degradation risk into five categories: very low I (0-0.2), low II (0.2-0.4), medium III (0.4-0.6), high IV (0.6-0.8), and very high V (0.8-1.0).

[0137] The power function fitting equation obtained in this embodiment is:

[0138] y=0.9948x 0.1095

[0139] Coefficient of determination R 2 =0.9993, indicating that the power function fitting accuracy meets the accuracy requirements.

[0140] Figure 3 A comparison chart shows the total mutation membership values ​​and risk assessment values ​​before and after the power function fitting improvement. It can be seen that before the improvement, the total mutation membership values ​​were generally too high and concentrated, making it difficult to intuitively distinguish the stages of wetland degradation risk in each year. After the improvement, the risk assessment values ​​have a larger range, a more intuitive and reasonable distribution of values, and a wider range of differences between years. This provides a higher level of resolution and a clearer definition of "good" and "bad." The numerical values ​​are more consistent with people's habit of intuitively determining risk based on the size of the assessment value.

[0141] Table 6 shows the results of the wetland degradation risk assessment for a specific region from 2000 to 2020, based on this example. It can be seen that the overall change in the wetland degradation risk level for the region showed a fluctuating upward trend from 0.0986 in 2000 to 0.4973 in 2020, an increase of 80.01%. In terms of risk level, the wetland degradation risk in the region remained at a moderate level over the 21 years.

[0142] Table 6

[0143]

[0144]

[0145] Example 2

[0146] Based on Example 1, a wetland degradation risk assessment system is provided, including:

[0147] The indicator candidate set building module is configured to: establish an evaluation indicator candidate set;

[0148] The indicator screening module is configured to: screen out independent evaluation indicators from the evaluation indicator candidate set through trial and error method according to the variance inflation coefficient (VIF) value of the evaluation indicator;

[0149] The comprehensive weight calculation module is configured to: use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct the comprehensive weight, and calculate the comprehensive weight of the selected independent evaluation indicators;

[0150] The system construction module is configured to: sort the evaluation indicators according to the size of the comprehensive weight, and construct a multi-level risk evaluation indicator system according to the sorting of the evaluation indicators;

[0151] The standardization processing module is configured to: standardize the original evaluation index data in the risk evaluation index system using the min-max standardization method;

[0152] The recursive operation module is configured to: select a corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system, perform recursive operation, and obtain a total mutation membership value;

[0153] A power function fitting module is configured to: convert the total mutation membership value into a risk assessment value using a power function fitting method;

[0154] The risk level determination module is configured to compare the risk assessment value with the risk level threshold and determine the wetland degradation risk level according to the comparison result.

[0155] This paper proposes a new wetland degradation risk assessment method by coupling the AHP, EWM and ICT methods. Compared with the CT method, the method has more accurate evaluation results, and the implementation process is simple and easy to understand. It has strong applicability and provides a new idea for studying wetland degradation risks.

[0156] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0160] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A wetland degradation risk assessment method, characterized in that: The method comprises: Step 1: Establish a candidate set of evaluation indicators, including: selecting human factors, natural factors, and socioeconomic factors that affect wetland degradation in the area to be evaluated, and combining them with wetland degradation characterization factors to form a candidate set of evaluation indicators; the candidate set of evaluation indicators includes: groundwater extraction, surface water supply, precipitation, evaporation, water yield coefficient, GDP per capita, patch density, normalized difference vegetation index, wetland area, and overall connectivity index; Step 2: Based on the Variance Inflation Factor (VIF) value of the evaluation indicator, independent evaluation indicators are selected from the evaluation indicator candidate set through trial and error method; Step 3: Use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct a comprehensive weight and calculate the comprehensive weight of the selected independent evaluation indicators, including: Among them, W i is the comprehensive weight of the i-th indicator, α i is the weight of the i-th indicator calculated by AHP, β i The weight of the ith indicator calculated for EWM, the larger the comprehensive weight, the more important the indicator; Step 4: Sort the evaluation indicators according to the size of the comprehensive weights, and construct a multi-level risk evaluation indicator system based on the ranking of the evaluation indicators, including: External pressure factors facing wetlands are divided into a "stress degree" system, and internal factors representing wetland degradation are divided into a "degradation degree" system. An evaluation index system consisting of a target layer, a criterion layer, and an indicator layer was constructed based on the principle that the greater the comprehensive weight of the indicator, the higher the indicator ranking. The target layer is the wetland degradation risk assessment value, and the criterion layer is the stress degree and degradation degree. Step 5: Use the min-max standardization method to standardize the original evaluation index data in the risk assessment index system; Step 6: Select the corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system and perform recursive operation to obtain the total mutation membership value; In step 6, the normalization formula of the catastrophe theory CT is used to perform recursive operation to obtain the total mutation membership value; Catastrophe theory (CT) divides system variables into state variables and control variables. State variables reflect the system's behavior and are the target of evaluation. Control variables are external factors affecting the system's operation and are the evaluation indicators. Commonly used catastrophe types include those with a one-dimensional state variable and no more than four-dimensional control variables, including wrinkles, cusps, swallowtails, and butterflies. The mutation type is wrinkle: the state variable is 1-dimensional, the control variable is 1-dimensional, and the potential function is f x =x 3 +ax, the normalization formula uses The schematic diagram is The mutation type is cusp: the state variable is 1-dimensional, the control variable is 2-dimensional, and the potential function is f x =x 4 +ax 2 +bx, the normalization formula is The schematic diagram is The mutation type is swallowtail: the state variable is 1-dimensional, the control variable is 3-dimensional, and the potential function is f x =x 5 +ax 3 +bx 2 +cx, the normalization formula is The schematic diagram is The mutation type is butterfly: the state variable is 1-dimensional, the control variable is 4-dimensional, and the potential function is f x =x 6 +ax 4 +bx 3 +cx 2 +dx, the normalization formula uses The schematic diagram is When the number of mutation type control variables exceeds 4, the rules of the potential functions of the fold, cusp, swallowtail and butterfly mutation types are summarized, and the mutation type potential function f with n-dimensional control variables is deduced. x : f x =x n+2 +a1x n +a2x n-1 +a3x n-2 +…+a n x Among them, f x is the potential function; x represents the state variable; n is the number of control variables; a i (i=1,2,3…n) represents n-dimensional control variables; Summarizing the rules of calculating normalized formulas for potential functions of fold, cusp, swallowtail and butterfly mutation types, we can deduce the normalized formula containing n-dimensional control variables: Among them, x i (i=1,2,3…n) is the membership value of the n-dimensional control variable; In the recursive operation process, if the control variables (1, 2, 3, ..., n) of the subsystem can complement each other, then the mutation membership value of the control variables of the upper layer system tends to the average value x = (x1 + x2 + x2 + ... + x n ) / n; If the control variables (1,2,3,…,n) of the subsystem cannot complement each other, the mutation membership value of the control variable of the upper layer system takes the minimum value, that is, x=min{x1,x2,x3,…,x n }; Step 7: converting the total mutation membership value into a risk assessment value using a power function fitting method, including: Take the standardized values ​​of all underlying indicators as x i (i=1,2,…,n), determine the mutation type of each subsystem according to the established evaluation index system, and recursively calculate the total mutation membership value y i (i=1,2,…,n); when n is large enough, establish the total mutation membership value y i with the normalized value x of the underlying indicator i The power function relationship; by determining the coefficient R 2 Determine the degree of curve fitting, coefficient R 2 The closer the value is to 1, the better the fit; According to the obtained power function relationship, the total mutation membership value y i Converted into a unified risk assessment value y i '; Step 8: Compare the risk assessment value with the risk level threshold, and determine the wetland degradation risk level based on the comparison result.

2. The method according to claim 1, wherein The step 2 includes: selecting evaluation indicators with a VIF not greater than 10 from the evaluation indicator candidate set as independent evaluation indicators.

3. The method according to claim 2, wherein Evaluation indicators with a VIF of no more than 5 were selected as independent evaluation indicators.

4. The method according to claim 1, wherein In step 5, the standardization method includes: For positive indicators, the standardized formula is: Where x′ i is the normalized value of the ith data of the positive indicator x; i is the i-th original value of the positive indicator x; x max is the maximum value of the positive indicator x; min is the minimum value of the positive indicator x; For negative indicators, the standardized formula is: Among them, y′ i is the normalized value of the i-th data of the negative indicator y; i is the i-th original value of the negative indicator y; y max is the maximum value of the negative indicator y; min It is the minimum value of the negative indicator y.

5. A wetland degradation risk assessment system, characterized in that: include: The indicator candidate set construction module is configured to: establish an evaluation indicator candidate set, including: selecting human factors, natural factors, and socioeconomic factors that affect wetland degradation in the area to be evaluated, and jointly forming an evaluation indicator candidate set with wetland degradation characterization factors; the evaluation indicator candidate set includes: groundwater extraction, surface water supply, precipitation, evaporation, water yield coefficient, per capita GDP, patch density, normalized difference vegetation index, wetland area, and overall connectivity index; The indicator screening module is configured to: screen out independent evaluation indicators from the evaluation indicator candidate set through trial and error method according to the variance inflation coefficient (VIF) value of the evaluation indicator; The comprehensive weight calculation module is configured to: use the analytic hierarchy process (AHP)-entropy weight method (EWM) to construct the comprehensive weight, and calculate the comprehensive weight of the selected independent evaluation indicators, including: Among them, W i is the comprehensive weight of the i-th indicator, α i is the weight of the i-th indicator calculated by AHP, β i The weight of the ith indicator calculated for EWM, the larger the comprehensive weight, the more important the indicator; The system construction module is configured to: rank the evaluation indicators according to the size of the comprehensive weight, and construct a multi-level risk assessment indicator system based on the ranking of the evaluation indicators, including: dividing the external pressure factors facing wetlands into a "stress degree" system, and dividing the internal factors representing wetland degradation into a "degradation degree" system; constructing an evaluation indicator system containing a target layer, a criterion layer, and an indicator layer according to the principle that the greater the comprehensive weight of the indicator, the higher the indicator ranking; the target layer is the wetland degradation risk assessment value, and the criterion layer is the stress degree and degradation degree; The standardization processing module is configured to: standardize the original evaluation index data in the risk evaluation index system using the min-max standardization method; The recursive operation module is configured to: select a corresponding normalization formula according to the number of control variables contained in each layer of the risk assessment index system to perform recursive operation to obtain the total mutation membership value; use the normalization formula of the mutation theory CT to perform recursive operation to obtain the total mutation membership value; Catastrophe theory (CT) divides system variables into state variables and control variables. State variables reflect the system's behavior and are the target of evaluation. Control variables are external factors affecting the system's operation and are the evaluation indicators. Commonly used catastrophe types include those with a one-dimensional state variable and no more than four-dimensional control variables, including wrinkles, cusps, swallowtails, and butterflies. The mutation type is wrinkle: the state variable is 1-dimensional, the control variable is 1-dimensional, and the potential function is f x =x 3 +ax, the normalization formula uses The schematic diagram is The mutation type is cusp: the state variable is 1-dimensional, the control variable is 2-dimensional, and the potential function is f x =x 4 +ax 2 +bx, the normalization formula is The schematic diagram is The mutation type is swallowtail: the state variable is 1-dimensional, the control variable is 3-dimensional, and the potential function is f x =x 5 +ax 3 +bx 2 +cx, the normalization formula is The schematic diagram is The mutation type is butterfly: the state variable is 1-dimensional, the control variable is 4-dimensional, and the potential function is f x =x 6 +ax 4 +bx 3 +cx 2 +dx, the normalization formula uses The schematic diagram is When the number of mutation type control variables exceeds 4, the rules of the potential functions of the fold, cusp, swallowtail and butterfly mutation types are summarized, and the mutation type potential function f with n-dimensional control variables is deduced. x : f x =x n+2 +a1x n +a2x n-1 +a3x n-2 +…+a n x Among them, f x is the potential function; x represents the state variable; n is the number of control variables; a i (i=1,2,3…n) represents n-dimensional control variables; Summarizing the rules of calculating normalized formulas for potential functions of fold, cusp, swallowtail and butterfly mutation types, we can deduce the normalized formula containing n-dimensional control variables: Among them, x i (i=1,2,3…n) is the membership value of the n-dimensional control variable; In the recursive operation process, if the control variables (1, 2, 3, ..., n) of the subsystem can complement each other, then the mutation membership value of the control variables of the upper layer system tends to the average value x = (x1 + x2 + x2 + ... + x n ) / n; If the control variables (1,2,3,…,n) of the subsystem cannot complement each other, the mutation membership value of the control variable of the upper layer system takes the minimum value, that is, x=min{x1,x2,x3,…,x n }; The power function fitting module is configured to: convert the total mutation membership value into a risk assessment value using a power function fitting method, including: taking the standardized values ​​of all underlying indicators as x i (i=1,2,…,n), determine the mutation type of each subsystem according to the established evaluation index system, and recursively calculate the total mutation membership value y i (i=1,2,…,n); when n is large enough, establish the total mutation membership value y i with the normalized value x of the underlying indicator i The power function relationship; by determining the coefficient R 2 Determine the degree of curve fitting, coefficient R 2 The closer the value is to 1, the better the degree of fit; according to the power function relationship obtained, the total mutation membership value y i Converted into a unified risk assessment value y i '; The risk level determination module is configured to compare the risk assessment value with the risk level threshold and determine the wetland degradation risk level according to the comparison result.

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