A method for dynamic evaluation and diagnosis of water resource carrying capacity

Abstract

This invention discloses a dynamic evaluation and diagnosis method for water resource carrying capacity, comprising: constructing an index system for water resource carrying capacity evaluation and calculating the weight of each evaluation index; dynamic evaluation of water resource carrying capacity based on time-varying VIKOR and diagnosis of the binary coupling driving effect of the indexes; when calculating the comprehensive score of water resource carrying capacity, this invention considers the cumulative effect of multi-year changes and quantifies the historical change characteristics of water resource carrying capacity by calculating the multi-year change amount and weighted change rate; this invention characterizes the evolution trend of water resource carrying capacity over time by calculating the change direction and rate of water resource carrying capacity.

Description

Technical Field

[0001] This invention relates to the field of water resource optimization and allocation, and specifically to a method for dynamic evaluation and diagnosis of water resource carrying capacity. Background Technology

[0002] Water resource carrying capacity refers to the maximum capacity of a region's water resources to support socio-economic development under certain living and technological conditions, while maintaining a healthy ecological environment. Improving water resource carrying capacity is beneficial for the rational allocation and efficient utilization of water resources and for sustainable economic and social development. Therefore, it is necessary to dynamically evaluate and diagnose the state of regional water resource carrying capacity in order to formulate and adjust control measures in advance, thereby achieving the rational allocation and utilization of water resources.

[0003] In its previously filed patent (patent number ZL202410587158.7, invention title: A Method for Predicting and Simulating Water Resource Carrying Capacity), the applicant constructed an indicator system and conducted secondary screening, simulating a large number of future values ​​of the indicators. The predicted water resource carrying capacity consists of multiple values, which are then analyzed from a probabilistic and statistical perspective. However, this water resource carrying capacity evaluation method is a static evaluation, and the calculation results are only instantaneous, representing the state of water resource carrying capacity at a specific time. It is a "retrospective summary" of historical states and cannot reflect the temporal changes in historical water resource carrying capacity. Consequently, it affects the reasonable judgment of the future state of water resource carrying capacity and is difficult to support the dynamic management of regional water resources.

[0004] Furthermore, water resource carrying capacity is influenced by multiple factors, and the same factor can have both positive and negative effects on water resource carrying capacity. Revealing the role of each factor in water resource carrying capacity from only one perspective (positive or negative) can lead to inaccurate identification of key factors, increased regulation costs, and trial-and-error risks. Therefore, a method is needed to dynamically evaluate water resource carrying capacity and comprehensively diagnose evaluation indicators, revealing the dual coupling effect of water resource carrying capacity evaluation indicators. Summary of the Invention

[0005] This invention addresses the shortcomings of existing water resource carrying capacity evaluation methods, which fail to reflect the temporal changes in historical water resource carrying capacity and only reveal the effects of each indicator on water resource carrying capacity from one perspective. Instead, it provides a dynamic evaluation and diagnosis method for water resource carrying capacity that reflects the temporal changes in historical water resource carrying capacity and demonstrates the positive and negative effects of each evaluation indicator on water resource carrying capacity.

[0006] The present invention adopts the following technical solution:

[0007] A method for dynamic evaluation and diagnosis of water resource carrying capacity includes the following steps:

[0008] Step 1: Construct an indicator system for evaluating water resource carrying capacity: Collect data on water resources, socio-economic conditions, ecological environment, and pollutant emissions in the study area, select multiple evaluation indicators based on the characteristics of the study area, and construct an indicator system for evaluating water resource carrying capacity.

[0009] Step 2: Calculate the weight of each indicator in Step 1;

[0010] Step 3: Dynamic evaluation of water resource carrying capacity based on time-varying VIKOR. Multiple samples are generated according to the number of cities in the study area and the study year. The dynamic evaluation includes the following steps:

[0011] Step 31: Calculate the positive ideal solution and the negative ideal solution;

[0012] Step 32: Calculate the group benefit value and the individual regret value;

[0013] Step 33: Calculate the water resource carrying capacity evaluation value for each sample;

[0014] Step 34: Construct a matrix of water resource carrying capacity evaluation values ​​for the study area;

[0015] Step 35: Calculate the standard deviation of the multi-year water resource carrying capacity assessment values ​​for a certain city;

[0016] Step 36: Calculate the direction and rate of change of the city's water resource carrying capacity in Step 35;

[0017] Step 37: Calculate the comprehensive score of the city's multi-year water resource carrying capacity in Step 35;

[0018] Step 4, diagnosis of the binary coupling driving effect of the indicators;

[0019] Based on the obstacle degree formula and the geographic detector method, the obstacle degree and driving force of the indicators on the development of water resource carrying capacity are calculated. Based on the obstacle degree and driving force of the indicators, the binary coupling driving effect of the indicators is constructed.

[0020] Furthermore, in step 2, information entropy and CRITIC are combined to calculate the weights of each indicator.

[0021] Furthermore, the weights of each indicator are calculated by combining information entropy and CRITIC, mainly including the following steps:

[0022] a. Standardize the indicators into positive and negative types separately, and calculate the standard deviation of each indicator. Sj Information entropy DJ and contrast R j :

[0023]

[0024] In the formula, x ij For the first j The first indicator i Standardized values ​​of a sample n For the sample size, For the first j The average of the indicators, r aj For the first a The first indicator and the first j The correlation coefficients between the indicators.

[0025] b. Calculate indicator weights

[0026]

[0027] in, m This represents the total number of indicators.

[0028] Furthermore, the specific calculation process for the dynamic evaluation of water resource carrying capacity based on time-varying VIKOR in step 3 is as follows:

[0029] Step 31: Calculate the ideal solution and negative ideal solution :

[0030]

[0031] In the formula, the operator max j , min j These represent the maximum and minimum values ​​of index j among the standardized values ​​of all samples, respectively.

[0032] Step 32: Calculate the group benefit value S i and individual regret value R i :

[0033]

[0034] In the formula, W j It is the weight of the j-th indicator.

[0035] Step 33: Calculate the water resource carrying capacity evaluation value for each sample. q i :

[0036]

[0037] In the formula, v This is the decision coefficient, typically set to 0.5.q i The smaller the size, the better the water resource carrying capacity.

[0038] Step 34: Construct a matrix of water resource carrying capacity evaluation values ​​for the study area. Q:

[0039]

[0040] In the formula, q tk It is the water resource carrying capacity assessment value of the k-th city in year t. q tk In the corresponding formula (9) q i , q i It is the water resource carrying capacity evaluation value of the i-th sample. The sample information includes the city and the year.

[0041] Step 35: Calculate the standard deviation of the multi-year water resources carrying capacity assessment value of the k-th city. SD k

[0042]

[0043] In the formula, denoted by , represents the average value of the water resources carrying capacity assessment for the k-th city over many years, and T represents the total number of years in the assessment period.

[0044] This method uses standard deviation to calculate the change in water resource carrying capacity, which ensures the consistency of data dimensions. This study evaluates all available data as a whole to ensure the comparability of assessment results across both time and space.

[0045] Step 36: Calculate the direction and rate of change of the water resource carrying capacity of the kth city. C k

[0046]

[0047] In the formula, WT It is a time-dynamic weight, when C k When the value is less than 0, it indicates that the water resource carrying capacity is improving.

[0048] This method introduces time weighting into the calculation of the multi-year change rate, giving greater weight to the assessment results of more recent years. This makes the multi-year change rate more sensitive to the latest changes, while also highlighting the long-term changing trend of water resource carrying capacity.

[0049] Step 37: Calculate the comprehensive score of the multi-year water resource carrying capacity of the kth city.Q k

[0050]

[0051] In the formula, q Tk It is the evaluation value of the water resource carrying capacity of the kth city in the last year of the evaluation period (if the evaluation period is 2003-2020, then T is 2020). It avoids complex weighting of the data for each year, and instead makes corrections based on the last year.

[0052] In water resource carrying capacity assessments, data from the most recent year typically best reflects the current situation. C k × SD k This is used to consider the cumulative effect or volatility of changes over many years.

[0053] Furthermore, the formulas for calculating the resistance and driving force in step 4 are as follows:

[0054]

[0055] In the formula, O ij It is the first j The first indicator i The distance between the standardized value of each sample and "1"; W j It is an indicator j The weights; Q j It is an indicator j The degree of obstruction; q j It is an indicator j The driving force ranges from 0 to 1; h represents the number of layers. N jh It is the first j The first indicator in the h Number of samples per layer; It is the first j The first indicator in the h The sample variance of the layer; It is the total variance.

[0056] Furthermore, in step 4, based on indicators j The degree of obstruction and driving force, constructing indicators j The binary coupling driving effect is:

[0057]

[0058] In the formula, F j As an indicatorj The binary coupling driving effect; according to the definition and calculation formula of the barrier degree, the sum of the barrier degrees of all indicators is 1. Therefore, the indicators with a barrier degree greater than the average barrier degree are defined as high barrier degree indicators (Q). j ≥1 / n), otherwise it is a low-impedance indicator; according to the definition and calculation formula of the geographic detector, the driving force of each indicator is between 0 and 1. Therefore, indicators with a driving force greater than 0.5 are defined as high-impedance indicators (q). j ≥0.5), otherwise it is a low driving indicator.

[0059] This invention reveals the hindering and driving effects of indicators on water resource carrying capacity. Indicators can be categorized into four types: high hindering-high driving, high hindering-low driving, low hindering-low driving, and low hindering-high driving. This approach facilitates the establishment of a differentiated monitoring system that prioritizes key indicators and routinely monitors general indicators, identifying "key control targets," and formulating differentiated control strategies to reduce control costs and trial-and-error risks. For high hindering-high driving indicators, as the "key hub" of carrying capacity evolution, their strong bidirectional influence determines the sensitivity of their control and they should be listed as core dynamic control targets. For high hindering-low driving indicators, as the "rigid bottleneck" hindering water resource carrying capacity, their hindering effect directly restricts system breakthroughs and lacks driving potential to offset constraints; they should be given key attention and steadily improved while ensuring no further deterioration. For low hindering-high driving indicators, as the "driving engine" for carrying capacity improvement, their driving effect is clear and the constraints are weak; they should be listed as targets for enhanced and amplified control. For low hindering-low driving indicators, as "auxiliary monitoring targets" of the carrying capacity system, their impact is weak and the risk of transformation is low; only routine tracking and monitoring are required.

[0060] The beneficial effects of this invention are as follows:

[0061] This invention, in calculating the comprehensive score of water resource carrying capacity, considers the cumulative effect of multi-year changes. It quantifies the historical characteristics of water resource carrying capacity changes by calculating the multi-year change amount and weighted change rate. This invention characterizes the evolutionary trend of water resource carrying capacity over time by calculating the direction and rate of change, rather than merely relying on static numerical comparisons. Furthermore, this invention introduces a time weight in the calculation of the multi-year change rate, giving greater weight to assessment results from more recent years. This makes the multi-year change rate more sensitive to recent changes and also highlights the long-term changing trend of water resource carrying capacity.

[0062] This invention calculates the driving force and hindering effect of each indicator, reflecting both positive and negative effects of the same indicator on water resource carrying capacity. This overcomes the problems of existing technologies that only reveal the role of each indicator in water resource carrying capacity from one perspective (positive or negative), leading to inaccurate identification of key factors and increased regulation costs and trial-and-error risks. This invention deeply reveals the dual-coupling driving effect of indicators on water resource carrying capacity, providing a reference for optimizing regional water resource allocation, improving the ecological environment, adjusting industrial structure, and optimizing urban population spatial distribution. Attached Figure Description

[0063] Figure 1 This is a flowchart of the method;

[0064] Figure 2 The results show a comparison of the three weighting methods;

[0065] Figure 3 This is a four-quadrant diagram of resistance and driving force. Detailed Implementation

[0066] To make the objectives, technical solutions, and technical effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0067] Example 1: This example takes 13 cities in the Beijing-Tianjin-Hebei urban agglomeration as the study area and uses this method to analyze the changes in water resource carrying capacity of these cities over 18 years from 2003 to 2020, with a total of 234 samples.

[0068] Step 1: Constructing an evaluation index system: Collect data on water resources, socio-economic conditions, ecological environment, and pollutant emissions within the study area. Based on the characteristics of the study area, select multiple indicators to construct an index system for evaluating water resource carrying capacity. The final index system established through data collection is shown in Table 1 below.

[0069] Table 1. Evaluation Index System for Water Resources Carrying Capacity

[0070]

[0071] Water resource carrying capacity is a complex and vast system; the larger the indicator system, the better its representativeness of water resource carrying capacity. However, due to limitations in data availability, this embodiment sets the evaluation scale to an annual scale and a city-level scale. Furthermore, to achieve evaluation over the longest possible historical period, some indicators were discarded. In constructing the indicator system, to ensure a balance between subsystems, this embodiment selected five indicators with relatively complete and representative data for each subsystem.

[0072] Step 2, calculate the weights of the water resources carrying capacity evaluation indicators:

[0073] By combining information entropy with CRITIC, the weights of each indicator in Table 1 are calculated according to formulas (1), (2), (3), and (4), and the calculation results are shown in Table 2.

[0074] Table 2 Weights of each evaluation indicator

[0075]

[0076] Figure 2 This paper compares the weighting results of water resource carrying capacity assessment indicators calculated using the entropy weighting method, CRITIC, and entropy-CRITIC. Significant differences exist in the weights calculated by the entropy weighting method, especially with the weight of X16 being almost 35 times that of X19. However, excessively large weight differences between indicators can severely weaken the effect of some indicators, rendering the constructed complex indicator system meaningless. While the conventional CRITIC method considers both the information content and conflict of indicators when calculating weights, its weight distribution is too even, making it difficult to distinguish the different effects of each indicator. The entropy-CRITIC method can simultaneously consider information entropy and CRITIC, highlighting individual indicators while avoiding overly even weight distribution.

[0077] Step 3: Based on the dynamic evaluation of water resource carrying capacity using time-varying VIKOR, the standard deviation of the multi-year water resource carrying capacity evaluation value of each study city is calculated using formula (11), the direction and rate of change of water resource carrying capacity of each study city are calculated using formula (12), and the comprehensive score of multi-year water resource carrying capacity of each study city is calculated using formula (14), which is the time-varying VIKOR result in Table 3.

[0078] Table 3. Water resource carrying capacity assessment results considering time-varying characteristics

[0079]

[0080] The multi-year average water resource carrying capacity assessment values ​​based on the conventional VIKOR method mentioned in Table 3 are calculated using the conventional VIKOR method. Table 4 shows the water resource carrying capacity assessment values ​​calculated using the conventional VIKOR method, i.e., calculated according to formula (9). q i , q i It is the water resource carrying capacity evaluation value corresponding to the i-th sample. In this case study, there are 13 cities in 18 years, so there are a total of 234 samples, which are then transformed into the matrix form of formula (10).

[0081] Table 4. Water resource carrying capacity evaluation values ​​based on conventional VIKOR.

[0082]

[0083] The rate and direction of change in Table 3 reveal the dynamic characteristics and trends of water resource carrying capacity over time. Overall, the water resource carrying capacity assessment values ​​of the 13 cities in the Beijing-Tianjin-Hebei region all show a downward trend (reflected by the direction of change), indicating that the water resource carrying capacity of the region is improving year by year. Specifically, Beijing ranks first in the multi-year average water resource carrying capacity assessment value based on the conventional VIKOR method, but this is due to its ranking advantage during the period from 2003 to 2018 and does not reflect the ranking changes during 2019 to 2020. In contrast, the time-varying VIKOR method shows that Beijing ranks third in water resource carrying capacity, a result that reflects the latest changes in Beijing's water resource carrying capacity status compared to the average. Tianjin, Qinhuangdao, and Chengde also show the same phenomenon. Xingtai, Baoding, and Tangshan show the opposite trend. Taking Tangshan, which has the most significant ranking change, as an example, its water resource carrying capacity ranking remained between 11th and 13th during the period from 2004 to 2017, which explains its average water resource carrying capacity ranking of 12th during this period. However, between 2018 and 2020, Tangshan's water resource carrying capacity ranking rose significantly, even reaching third place in 2019. Therefore, Tangshan's water resource carrying capacity ranking, calculated based on time-varying VIKOR, rose to sixth place. Furthermore, although the time-weighted VIKOR rankings of Shijiazhuang, Hengshui, Cangzhou, and Langfang were largely consistent with their multi-year average rankings, these cities are all improving their water resource carrying capacity at different rates.

[0084] Comparing the results of conventional VIKOR and time-varying VIKOR reveals that conventional VIKOR only reveals the state of water resource carrying capacity at a specific moment. While comparing evaluation values ​​from different years can indicate how water resource carrying capacity changes, it cannot provide a detailed understanding of the specific characteristics of water resource carrying capacity changes over time. In contrast, time-varying VIKOR, by introducing time weights, quantifies the rate and direction of change of water resource carrying capacity over many years. This quantification reveals the evolutionary trend of carrying capacity (such as continuous improvement, rapid deterioration, or fluctuating stability), rather than merely remaining at the level of static numerical comparisons. Furthermore, conventional VIKOR is a "retrospective summary" of historical states and cannot reflect the impact of recent changes on the future. Time-varying VIKOR, by strengthening the weight of recent data through time weighting, can promptly capture the latest trends in carrying capacity changes, providing support for predicting future states (such as whether the carrying capacity threshold is about to be exceeded) and avoiding the lag of static evaluations.

[0085] Step 4, Diagnosis of the dual coupling driving effect of indicators: Calculate the degree of obstruction of the indicators to the development of water resource carrying capacity according to formula (16), and calculate the driving force of the indicators to the development of water resource carrying capacity according to formula (17). The calculation results are as follows: Figure 3As shown in the figure, HH represents high resistance - high driving factor, LL represents low resistance - low driving factor, and HL represents high resistance - low driving factor. Figure 3 The study clearly demonstrates the magnitude of the hindering and driving forces of each indicator on water resource carrying capacity. The hindering effect reflects how much an indicator's current state impedes water resource carrying capacity, while the driving force reflects how much improving an indicator enhances water resource carrying capacity. Therefore, quantifying the dual-coupling driving effect of an indicator facilitates policymakers in developing more targeted control strategies. Revealing the role of an indicator on water resource carrying capacity from only one perspective (hindering or driving) fails to comprehensively reveal its impact and may lead to control measures that fail to produce the expected results. For example, if an indicator is a high hindering factor but also a low driving factor, identifying only its hindering effect on water resource carrying capacity may result in wasted resources when relevant departments spend significant resources optimizing this indicator, leading to unintended consequences. Conversely, identifying only its low driving factor might lead departments to ignore the indicator, allowing it to remain unchanged and creating potential future problems for water resource carrying capacity.

[0086] It should be understood that any parts not described in detail in this invention belong to the prior art.

[0087] The above description, in conjunction with the accompanying drawings, is merely a specific implementation method and process of the present invention. However, the scope of protection of the present invention is not limited thereto. Any person skilled in the art should understand that this is only an illustrative example, and various changes and substitutions can be made to this implementation method without departing from the essence of the present invention. The scope of the present invention is defined only by the appended claims.

Claims

1. A water resource carrying capacity dynamic evaluation and diagnosis method, characterized in that, The method comprises the following steps: Step 1, constructing an index system for evaluating water resources carrying capacity: collecting data about water resources, economic society, ecological environment and pollution gas emission in a research region, selecting multiple evaluation indexes according to the characteristics of the research region, and constructing an index system for evaluating water resources carrying capacity; Step 2, calculating the weight of each index in step 1; Step 3, dynamic evaluation of water resources carrying capacity based on time-varying VIKOR: according to the number of cities in the research region and the research year, multiple samples are formed, and the dynamic evaluation comprises the following steps: Step 31, calculating the positive ideal solution and the negative ideal solution; Step 32, calculating the group benefit value and the individual regret value; Step 33, calculating the water resources carrying capacity evaluation value of each sample; Step 34, constructing a matrix of the water resources carrying capacity evaluation value of the research region; Step 35, calculating the standard deviation of the water resources carrying capacity evaluation value of a city in multiple years; The standard deviation of the water resources carrying capacity evaluation value of the kth city in many years SD k The calculation formula is: ; In the formula, T represents the total number of years in the evaluation period; q tk is the water resources carrying capacity evaluation value of the kth city in the tth year. Step 36, calculating the change direction and rate of the water resources carrying capacity of the city in step 35; The change direction and rate of the kth city water resources carrying capacity C k The calculation formula is: ; ; In the formula, WT is the time dynamic weight, when C k <0 indicates that the water resources carrying capacity is improving; Step 37, calculating the comprehensive score of the water resources carrying capacity of the city in multiple years in step 35; The comprehensive score of the water resource carrying capacity of the kth city in many years Q k The calculation formula is: ; In the formula, q Tk is the evaluation value of the water resources carrying capacity of the kth city in the last year within the evaluation period. Step 4, diagnosis of the binary coupling driving effect of the index: Based on the hindering degree formula and the geographic detector method, the hindering degree and the driving force of the index on the development of water resources carrying capacity are calculated, and the binary coupling driving effect of the index is constructed based on the hindering degree and the driving force of the index. ; In the formula, F j As an indicator j The binary coupling driving effect; Q j It is an indicator j The degree of obstruction; n is the number of samples; q j It is an indicator j The driving force; HH represents high resistance - high driving factor, HL represents high resistance - low driving factor, LH represents low resistance - high driving factor, and LL represents low resistance - low driving factor.

2. The water resource carrying capacity dynamic evaluation and diagnosis method according to claim 1, characterized in that, The hindering degree and the driving force calculation formula in step 4 is as follows: ; ; ; In the formula, O ij It is the first j The first indicator i The distance between the standardized value of each sample and "1"; x ij For the first j The first indicator i Standardized values ​​for each sample; W j It is an indicator j The weights; Q j It is an indicator j The degree of obstruction; q j It is an indicator j The driving force ranges from 0 to 1; h represents the number of layers. N jh It is the first j The first indicator in the h Number of samples per layer; It is the first j The first indicator in the h The sample variance of the layer; It is the total variance.

3. The water resource carrying capacity dynamic evaluation and diagnosis method according to claim 1, characterized in that, The specific calculation process of steps 31-34 is as follows: Step 31, calculate positive ideal solution and negative ideal solution : ; ; In the formula, the operators max j , min j respectively represent the maximum value and the minimum value of the index j among the standardized values of all samples. Step 32, calculating a group benefit value S i and individual regret values R i : ; ; wherein W j is the weight of the jth indicator; m is the total number of indicators; Step 33, calculate the water resources carrying capacity evaluation value of each sample q i : ; In the formula, v is a decision coefficient; Step 34, constructing a matrix of the evaluation value of water resources carrying capacity of the study area Q : ; In the formula, q tk is the water resources carrying capacity evaluation value of the kth city in the tth year.

4. The water resource carrying capacity dynamic evaluation and diagnosis method according to claim 1, characterized in that, In step 2, the weight of each index is calculated by combining information entropy with CRITIC.

5. The water resource carrying capacity dynamic evaluation and diagnosis method according to claim 4, characterized in that, The weight of each index is calculated by combining information entropy with CRITIC, mainly including the following steps: a. The indicators are divided into positive and negative types and standardized, and the standard deviation of each indicator is calculated Sj , information entropy dj and contrast R j : ; ; wherein x ij is the standardized value of the first j indicator for the first i sample, n is the number of samples, is the mean value of the first j indicator, r aj is the correlation coefficient between the first a indicator and the second j indicator. b. Calculate the index weight: ; wherein m is the total number of indices.

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