Water-carbon-ecosystem coupling coordination analysis method and prediction system

Abstract

The invention relates to the field of water resource management, and particularly discloses a water-carbon-ecology coupling coordination analysis method and a prediction system. The method comprises the following steps: determining evaluation indexes of a water resource system, a carbon emission system and an ecological system, obtaining water resource data, carbon emission data and ecological resource data, establishing an index evaluation system of each associated system, determining the weight of the index system of each associated system, and determining the weight of the index system of each associated system; and analyzing the coupling coordination degree of the water resource-carbon emission-ecosystem association system, and analyzing the coordination degree of the development of the water resource system, the carbon emission system and the ecosystem by using a WCENI index model and an ARIMA-SVM-GM model.

Description

Technical Field

[0001] This invention belongs to the field of water resource management, specifically relating to a water-carbon-ecosystem coupling coordination analysis method and prediction system. Background Technology

[0002] The water resource system, carbon emission system, and ecosystem are closely interconnected. Water resources are essential for the protection, restoration, and maintenance of ecosystem functions; a healthy ecosystem plays a crucial role in water conservation and carbon sequestration; and the management and utilization of water resources directly affect the stability of ecosystems and carbon cycle processes. The coupling and coordination of the "water-carbon-ecology" system directly relates to environmental carrying capacity, climate adaptability, and the level of sustainable development. Therefore, understanding the coupling relationship between "water-carbon-ecology" and exploring its system coordination is an inevitable choice for achieving ecological protection, low-carbon development, and harmonious coexistence between humans and nature.

[0003] In recent years, most research findings have focused on the relationships between single aspects or multiple systems within the "water-energy-food" and "water-soil-energy-carbon" systems. Research on the coupled and coordinated development and driving factors of the "water-carbon-ecology" system remains somewhat lacking. In terms of research content, international research primarily approaches the topic from the perspective of ecosystem services and resource flows, focusing on the interaction mechanisms between water cycle processes, carbon migration and transformation, and ecosystem functions. For example, by comparing the structure and function of wetland ecosystems in different climate zones, it reveals the key impacts of topography, hydrology, and vegetation type on the water-carbon-ecology relationship. Another example is the calculation of blue water consumption and carbon footprints for different land use types, proposing the need to coordinate water resource management, ecological protection, and carbon emission reduction to achieve regional sustainable development. Domestic research, on the other hand, focuses more on the needs of ecological civilization construction and green development, exploring the synergistic mechanisms of the "water-carbon-ecology" system under the background of climate change, while incorporating ecological security, low-carbon transformation, and high-quality development into the overall analytical framework. From a methodological perspective, there are two main approaches: First, top-down input-output models (such as the MRIO model). This method can systematically reveal the intrinsic connections between water use, carbon emissions, and ecosystem services across various industrial sectors within an economic system. It has a clear structure and is easily expandable, but there is still room for improvement in quantifying ecological value and categorizing sectors. Second, bottom-up life cycle assessments (such as the LCA model). This method emphasizes the integrity of the overall system boundary and can comprehensively track the water consumption, carbon emissions, and ecological impacts of products or systems throughout their entire life cycle. However, it requires high-quality data, and there is a lack of unified standards for defining system boundaries.

[0004] However, existing research has not explored the water-carbon-ecological coupling mechanism in sufficient depth. The selection of indicators is not comprehensive enough and is subjective, resulting in indicators that are not representative enough and cannot reflect the coupling and coordination characteristics of the three elements. Methodologically, there is no specific method for water-carbon-ecological coupling. Therefore, how to provide a simulation method and system for water, carbon, and ecological coupling is an urgent problem to be solved. Summary of the Invention

[0005] To address the current lack of methods for coordinating water-carbon-ecology coupling, this invention provides an analytical calculation method and prediction system for water-carbon-ecology coupling coordination.

[0006] This invention provides a method for analyzing the coupling and coordination of water, carbon, and ecosystems, comprising the following steps: Determine the evaluation indicators for the water resources subsystem, carbon emission subsystem, and ecological subsystem, and construct a coupled and coordinated evaluation indicator system for the water-carbon-ecology related system. The evaluation indicators for each subsystem were screened, and the important evaluation indicators for each subsystem were selected. The water-carbon-ecological coupling combination weight is calculated based on the important evaluation indicators of each subsystem, and the water-carbon-ecological coupling coordination index WCENI is calculated based on the coupling combination weight. A water-carbon-ecology coupling model was constructed to calculate the coupling coordination prediction results.

[0007] The above-mentioned water-carbon-ecosystem coupling coordination analysis method constructs a coupling coordination evaluation index system for water-carbon-ecosystem linkages based on the richness of a single system, the coordination between two systems, and the robustness of a third system.

[0008] The aforementioned water-carbon-ecosystem coupling and coordination analysis method includes the following evaluation indicators for the resource subsystem: per capita water consumption, per capita water resources, water resources development and utilization rate, water quality compliance rate of water function zones, water consumption per 10,000 yuan of GDP, and rainfall; the evaluation indicators for the carbon emission system include energy consumption, per capita GDP, energy consumption per unit of GDP, water consumption per 10,000 yuan of GDP, the proportion of primary industry output value to GDP, and chemical oxygen demand emissions; and the evaluation indicators for the ecosystem include forest coverage rate, ammonia nitrogen emissions, the proportion of ecological water consumption, sewage treatment rate, vegetation comprehensive index, and area of ​​soil and water conservation.

[0009] The above-described water-carbon-ecosystem coupling coordination analysis method selects important evaluation indicators for each subsystem based on the barrier degree of each evaluation indicator, including the following steps: (1) Calculate the contribution and deviation of each indicator in each subsystem; ; ; In the formula: Contribution to the indicator; These are the weight values ​​of the criteria layer corresponding to the indicators; Here is the weight value of indicator i; The deviation of the indicator; For the corresponding indicator values.

[0010] (2) Calculate the obstacle degree of each evaluation index. .

[0011] The above-mentioned analytical method for water-carbon-ecosystem coupling coordination, based on the fusion of the CRITIC method and the EFAST algorithm to calculate the coupling combination weights, includes the following steps: (1) Normalize the different important evaluation indicators in each system to eliminate the differences in the dimensions of different indicators; (2) Calculate the CRITIC weights; (3) Calculate the sensitivity weights based on the EFAST algorithm; (4) Calculate the weight of the water-carbon-ecological coupling combination.

[0012] The above-mentioned water-carbon-ecosystem coupling coordination analysis method, step (2) CRITIC weight calculation formula is as follows: ; ; ; In the formula: S j This represents the standard deviation of the j-th indicator; Indicates the corresponding indicator value; r ij This represents the correlation coefficient between evaluation indicators i and j; Indicates the amount of information contained in the indicator; This represents the CRITIC weight.

[0013] The sensitivity weight calculation formula in step (3) of the above water-carbon-ecosystem coupling coordination analysis method is as follows: ; ; ; ; In the formula: V is the sum of the variances of the coupling effects among the indicators; As an indicator The variance; As an indicator and Variance of interaction As an indicator , and Variance of interactions; As an indicator The variance through the interaction of the remaining n-1 indicators; As an indicator Second-order sensitivity index of coupling effect with other indicators ; It is a third-order sensitivity index; It is a higher-order sensitivity index; This is the sum of the sensitivity of each indicator; This represents the sensitivity weight of the indicator.

[0014] The above-mentioned water-carbon-ecosystem coupling coordination analysis method, in step (4), the formula for calculating the water-carbon-ecological coupling combination weight is as follows: ; In the formula: Here, n represents the weight value of the indicator combination; n is the number of indicators. This is the result of CRITIC weighting; This represents the sensitivity weight of the indicator.

[0015] The above-mentioned analytical method for water-carbon-ecosystem coupling coordination, the formula for calculating the water-carbon-ecological coupling coordination index WCENI is as follows: WCENI = WCENI ri + WCENI co + WCENI ro = ; In the formula: This represents the weight value of indicator i; Indicates the corresponding indicator value; WCENI ri WCENI represents the richness index of a single subsystem. co WCENI represents the coordination index between the two systems. ro This represents the robustness index of the three systems.

[0016] This invention also provides a water-carbon-ecosystem coupling coordination prediction system, including: an indicator system construction and data preprocessing module, used to determine the evaluation indicators of the water resources subsystem, carbon emission subsystem and ecological subsystem, and to screen the evaluation indicators of each subsystem, select the important evaluation indicators of each subsystem, and construct a coupling coordination evaluation indicator system for the water-carbon-ecological system. The combined weight calculation module is used to calculate the water-carbon-ecology coupling combined weight based on the important evaluation indicators of each subsystem. The coupling coordination index calculation module is used to calculate the water-carbon-ecology coupling coordination index WCENI based on the coupling combination weights. The multi-scenario simulation and prediction module is used to construct a water-carbon-ecology coupling model to predict the results of multi-scenario coupling and coordination.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a water-carbon-ecology coupled simulation method based on the WCENI index. This method deeply analyzes the coordinated evolution mechanism among the three subsystems of water resources, carbon emissions, and ecosystems, and constructs a corresponding index system to quantify their coordinated correlation characteristics. Relying on a multi-scale basic database of water-carbon-ecology, this method can achieve comprehensive integration and analysis of the complex relationships between water resources, carbon emissions, and ecosystems, making up for the shortcomings of current research on the complex correlations of water-carbon-ecology. This database not only focuses on water resources, carbon emissions, and ecosystems, but also broadly covers the causal relationship networks of complex systems such as society, economy, and environment, making the research results more reliable. By constructing the ARIMA-SVM-GM model, this invention establishes a multi-scenario simulation model for the "water-carbon-ecology" coupled system. This model can scientifically analyze the coupling relationship between "water-carbon-ecology" from multiple dimensions, including environmental, technological, and economic scales, thereby filling the current research gap in this field. It provides a powerful tool and support for a deeper understanding of the dynamic behavior of the "water-carbon-ecology" coupled system and the formulation of relevant strategies, and enables the research results to better serve the sustainable development goals and promote the coordinated management of water resources, carbon emissions, and ecosystems. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the calculation process of the water-carbon-ecosystem coupling coordination degree analysis method described in this invention; Figure 2 The indicators selected for the evaluation index system of the water-carbon-ecosystem of this invention; Figure 3 This is a schematic diagram illustrating the principle of the WCENI index in the water-carbon-ecosystem coupling simulation method according to the embodiments. Figure 4 This is a diagram illustrating the degree of coupling coordination in this invention. Figure 5 This is a diagram showing the weighting results of the water-carbon-ecosystem evaluation index system according to the embodiments. Figure 6 This is a graph showing the results of the WCENI index in the study area according to an embodiment; Figure 7 This is a schematic diagram illustrating the principle of the coupled simulation method for simulating different scenarios of water-carbon-ecosystem in an embodiment of the present invention; Figure 8 The simulation verification results of the ARIMA-SVM-GM model shown in the embodiments of the present invention; Figure 9 The parameter settings for the water-saving and water-protecting model shown in the embodiments of the present invention; Figure 10 The parameter settings for the energy-saving and emission-reduction model shown in the embodiments of the present invention; Figure 11 The parameter settings for the ecological priority model shown in the embodiments of the present invention; Figure 12 Parameter settings for the comprehensive and coordinated development model shown in the embodiments of the present invention; Figure 13 The above are multi-scenario prediction results shown in the embodiments of the present invention. Detailed Implementation

[0019] This invention proposes a method for calculating the coupling coordination degree of a water-carbon-ecology interconnected system and a simulation and prediction method for the coordinated development of water-carbon-ecology under different scenarios. It quantitatively decouples the coupling coordination degree among the three systems (water, carbon, and ecology) and their development trends under multiple scenarios, which is beneficial for improving the comprehensiveness of water resource assessment. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1: This example provides a method for calculating the coupling coordination degree of water-carbon-ecosystem, specifically including the following: Step 1: Determine the evaluation indicators for the water resources subsystem, carbon emission subsystem, and ecological subsystem respectively. Construct a coupling and coordination evaluation indicator system for the water-carbon-ecology related system based on the richness of a single system, the coordination between two systems, and the robustness of the third system. The evaluation indicators for the water resources subsystem include per capita water consumption, per capita water resources, water resources development and utilization rate, water quality compliance rate of water function zones, water consumption per 10,000 yuan of GDP, and rainfall. The carbon emission system evaluation indicators include energy consumption, GDP per capita, energy consumption per unit of GDP, water consumption per 10,000 yuan of GDP, the proportion of primary industry output to GDP, and chemical oxygen demand emissions. Ecosystem evaluation indicators include forest coverage, ammonia nitrogen emissions, ecological water consumption as a percentage of total water consumption, wastewater treatment rate, vegetation comprehensive index, and area of ​​soil and water conservation. Step 2: Calculate the barrier degree of each evaluation indicator in each subsystem, and select the important evaluation indicators for each subsystem based on the calculation results; this includes the following steps: (1) Calculate the contribution and deviation of each indicator in each subsystem;

[0021]

[0022] In the formula: Contribution to the indicator; These are the weight values ​​of the criteria layer corresponding to the indicators; Here is the weight value of indicator i; The deviation of the indicator; For the corresponding indicator values.

[0023] (2) Calculate the obstacle degree of each evaluation index.

[0024] Based on the obstacle level of each evaluation indicator, key evaluation indicators for each subsystem are selected; such as... Figure 2 As shown in this embodiment Step 3: Calculate the Water-Carbon-Ecology Coordination Index (WCENI), including the following steps: (1) Normalize the different important evaluation indicators in each system to eliminate the differences in the dimensions of different indicators; (2) Calculate the CRITIC weights. ; ; ;

[0025] In the formula: S j This represents the standard deviation of the j-th indicator; Indicates the corresponding indicator value; r ij This represents the correlation coefficient between evaluation indicators i and j; Indicates the amount of information contained in the indicator; This represents the CRITIC weight.

[0026] (3) Calculate sensitivity weights based on the EFAST algorithm ; ; ; ;

[0027] In the formula: V is the sum of the variances of the coupling effects among the indicators; As an indicator The variance; As an indicator and Variance of interaction As an indicator , and Variance of interactions; As an indicator The variance through the interaction of the remaining n-1 indicators; As an indicator Second-order sensitivity index of coupling effect with other indicators ; It is a third-order sensitivity index; It is a higher-order sensitivity index; This is the sum of the sensitivity of each indicator; This represents the sensitivity weight of the indicator.

[0028] (4) Calculate the weights of the water-carbon-ecological coupling combination. ;

[0029] In the formula: Here, n represents the weight value of the indicator combination; n is the number of indicators. This is the result of CRITIC weighting; This represents the sensitivity weight of the indicator.

[0030] Step 4: Calculate the comprehensive evaluation index of water-carbon-ecological coupling. WCENI = WCENI ri + WCENI co + WCENI ro = ;

[0031] In the formula: This indicates the weight value of the corresponding indicator; Indicates the corresponding indicator value; WCENI ri WCENI represents the richness index of a single subsystem. co WCENI represents the coordination index between the two systems. ro This represents the robustness index of the three systems.

[0032] Step 5: Construct the ARIMA-SVM-GM fusion model and calculate the predicted values; The ARIMA model is a classic time series analysis method with a strong ability to capture linear patterns. It can effectively identify and predict linear patterns such as deterministic trends and seasonal fluctuations in coupled series. Furthermore, its computational process is standardized and easy to implement, providing a reliable benchmark for hybrid models. The model's expression is as follows: ;

[0033] In the formula: This is the data sequence obtained after differencing the original sequence. and These are the parameters of the autoregressive moving average model.

[0034] The SVM model effectively maps a high-dimensional feature space through kernel function techniques, enabling it to keenly capture the complex nonlinear relationships and interactions within the "water-carbon-ecology" system. It overcomes the limitations of linear models and, based on the principle of minimizing structural risk, demonstrates good generalization performance and prediction accuracy even on small sample datasets. It is suitable for typical small-sample time-series data related to socioeconomic and ecological factors. The SVM model expression is as follows: ;

[0035] In the formula: and All are Lagrange multipliers; For bias; This is the kernel function.

[0036] The GM model has very low data requirements, needing only ≥4 data points to build the model. For monotonic sequences with obvious exponential growth or decay trends, it exhibits high prediction accuracy and stable short- to medium-term extrapolation results. The model expression is as follows: ; ; ; ; In the formula: a is the development gray number, and b is the endogenous control gray number; for These are the restored simulated values.

[0037] This study constructs a hybrid ARIMA-SVM-GM prediction model based on ARIMA, SVM, and GM models. This hybrid prediction model integrates the core advantages of the three individual models through a mechanism-data dual-driven framework: ARIMA accurately captures the linear trend and seasonal patterns of the coupling coordination degree sequence, laying the foundation for prediction; SVM leverages its powerful kernel function to learn the complex nonlinear interactions implicit in the residuals, improving fitting accuracy; and GM(1,1) is introduced to address information-poor scenarios with limited data in subsystems or regions, enhancing model robustness. This model effectively balances the accuracy of time series prediction, the ability to characterize system nonlinearity, and adaptability to small samples. It can more scientifically simulate the coupling coordination evolution path of the "water-carbon-ecology" system under different policy scenarios, providing quantitative basis for watershed sustainable development decision-making. The calculation steps are as follows: ; ; ; ;

[0038] In the formula: L represents the standard deviation of the k-th model; t This represents the actual observed value of the model at time point t; This represents the model's predicted value at time point t; n represents the number of historical data points used for evaluation. The coefficient of variation of model k; This represents the average of the predicted value sequence for model k; For the corresponding weight values ​​of ARMIA, SVM, and GM models; The combined weight values ​​for the ARIMA-SVM-GM model; , , These represent the predicted values ​​from the ARIMA model, SVM model, and GM model, respectively.

[0039] Experimental Case: This experimental case takes the eleven provinces and municipalities along the Yangtze River Economic Belt as the study area, specifically including Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou and Yunnan, covering an area of ​​approximately 2,052,300 square kilometers, accounting for 21.4% of the national total.

[0040] The weight values ​​of the evaluation index system for the water-carbon-ecosystem in the study area were calculated using the method in Example 1. The specific weight values ​​are as follows: Figure 5 As shown; Calculate the degree of coupling and coordination of water-carbon-ecosystem in the WCENI indexed study area, such as... Figure 4 As shown, the result is as follows Figure 6 The diagram showing the changes in the WCENI index illustrates the spatiotemporal evolution of the WCENI index values ​​in the eleven provinces and municipalities along the Yangtze River Economic Belt.

[0041] A hybrid ARIMA-SVM-GM prediction model was constructed, and the model's prediction results were compared with the measured data. Simultaneously, the ARIMA, SVM, and GM models were used to predict the values ​​and coupling degrees of key influencing factors in the study area. The accuracy and applicability of the prediction model were verified and finally selected by calculating the relative error between historical prediction values ​​and the actual dataset. The results are as follows: Figure 8 As shown, the simulation prediction results of the ARIMA-SVM-GM model are closest to the prediction results of the ARIMA model, SVM model, and GM model in the prediction set data, indicating that the ARIMA-SVM-GM model constructed in this invention has more accurate predictions and better applicability.

[0042] By comprehensively considering various factors such as the actual background of the study area, the development goals of relevant provinces and cities during the national planning period, and ecological and environmental protection measures, etc. Figure 7As shown, four development scenarios were set, specifically: water conservation and protection, energy conservation and emission reduction, ecological priority, and comprehensive and coordinated development. The simulation parameter calibration data for each scenario are as follows: Figure 9-12 This study reveals the evolutionary laws of system coupling coordination from different perspectives and the potential optimization directions for future development, providing theoretical basis and practical guidance for formulating scientific and reasonable development strategies, and realizing efficient resource utilization and sustainable development.

[0043] An ARIMA-SVM-GM combined model was used to simulate and predict the development levels of key influencing factors in the study area. Based on growth rate parameters under different scenarios, the data growth of the water-carbon-ecosystem under different scenarios was simulated, resulting in corresponding time-series data matrices. These matrices were then substituted into the ARIMA-SVM-GM combined model to calculate the predicted coupling coordination degree of the study area under different scenarios. Figure 12 As shown.

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the coupling and coordination of water-carbon-ecosystem, characterized in that: Includes the following steps: Determine the evaluation indicators for the water resources subsystem, carbon emission subsystem, and ecological subsystem, and construct a coupled and coordinated evaluation indicator system for the water-carbon-ecology related system. The evaluation indicators for each subsystem were screened, and the important evaluation indicators for each subsystem were selected. The water-carbon-ecological coupling combination weight is calculated based on the important evaluation indicators of each subsystem, and the water-carbon-ecological coupling coordination index WCENI is calculated based on the coupling combination weight. A water-carbon-ecology coupling model was constructed to calculate the coupling coordination prediction results.

2. The water-carbon-ecosystem coupling coordination analysis method according to claim 1, characterized in that: A coupling and coordination evaluation index system for the water-carbon-ecological system is constructed based on the criteria of the richness of a single system, the coordination between two systems, and the robustness of a third system.

3. The water-carbon-ecosystem coupling coordination analysis method according to claim 1, characterized in that: The evaluation indicators for the resource subsystem include per capita water consumption, per capita water resources, water resources development and utilization rate, water quality compliance rate of water function zones, water consumption per 10,000 yuan of GDP, and rainfall; the evaluation indicators for the carbon emission system include energy consumption, per capita GDP, energy consumption per unit of GDP, water consumption per 10,000 yuan of GDP, the proportion of primary industry output value to GDP, and chemical oxygen demand emissions; the evaluation indicators for the ecosystem include forest coverage rate, ammonia nitrogen emissions, the proportion of ecological water consumption, sewage treatment rate, vegetation comprehensive index, and area of ​​soil and water conservation.

4. The water-carbon-ecosystem coupling coordination analysis method according to claim 1, characterized in that: The key evaluation indicators for each subsystem are selected based on the obstacle level of each evaluation indicator, including the following steps: (1) Calculate the contribution and deviation of each indicator in each subsystem; ； ； In the formula: Contribution to the indicator; These are the weight values ​​of the criteria layer corresponding to the indicators; Here is the weight value of indicator i; The deviation of the indicator; The corresponding indicator value; (2) Calculate the obstacle degree of each evaluation index. 。 5. The water-carbon-ecosystem coupling coordination analysis method according to claim 1, characterized in that: The product is calculated based on the fusion of the CRITIC method and the EFAST algorithm, and includes the following steps: (1) Normalize the different important evaluation indicators in each system to eliminate the differences in the dimensions of different indicators; (2) Calculate the CRITIC weights; (3) Calculate the sensitivity weights based on the EFAST algorithm; (4) Calculate the weight of the water-carbon-ecological coupling combination.

6. The water-carbon-ecosystem coupling coordination analysis method according to claim 5, characterized in that: The formula for calculating the CRITIC weight in step (2) is as follows: ； ； ； In the formula: S j This represents the standard deviation of the j-th indicator; Indicates the corresponding indicator value; r ij This represents the correlation coefficient between evaluation indicators i and j; Indicates the amount of information contained in the indicator; This represents the CRITIC weight.

7. The water-carbon-ecosystem coupling coordination analysis method according to claim 6, characterized in that: The formula for calculating the sensitivity weight in step (3) is: ； ； ； ； In the formula: V is the sum of the variances of the coupling effects among the indicators; As an indicator The variance; As an indicator and Variance of interaction As an indicator , and Variance of interactions; As an indicator The variance through the interaction of the remaining n-1 indicators; As an indicator Second-order sensitivity index of coupling effect with other indicators ; It is a third-order sensitivity index; It is a higher-order sensitivity index; This is the sum of the sensitivity of each indicator; This represents the sensitivity weight of the indicator.

8. The water-carbon-ecosystem coupling coordination analysis method according to claim 7, characterized in that: The formula for calculating the weight of the water-carbon-ecological coupling combination in step (4) is as follows: ； In the formula: Here, n represents the weight value of the indicator combination; n is the number of indicators. The result is the CRITIC weighting. This represents the sensitivity weight of the indicator.

9. The water-carbon-ecosystem coupling coordination analysis method according to claim 1, characterized in that: The formula for calculating the water-carbon-ecological coupling coordination index (WCENI) is as follows: WCENI = WCENI ri + PRICES co + PRICES ro = ; In the formula: This represents the weight value of indicator i; Indicates the corresponding indicator value; WCENI ri WCENI represents the richness index of a single subsystem. co WCENI represents the coordination index between the two systems. ro This represents the robustness index of the three systems.

10. A water-carbon-ecosystem coupled coordination prediction system, characterized in that: include: The indicator system construction and data preprocessing module is used to determine the evaluation indicators for the water resources subsystem, carbon emission subsystem and ecological subsystem, and to screen the evaluation indicators for each subsystem, select the important evaluation indicators for each subsystem, and construct a coupled and coordinated evaluation indicator system for the water-carbon-ecology related system. The combined weight calculation module is used to calculate the water-carbon-ecology coupling combined weight based on the important evaluation indicators of each subsystem. The coupling coordination index calculation module is used to calculate the water-carbon-ecology coupling coordination index WCENI based on the coupling combination weights. The multi-scenario simulation and prediction module is used to construct a water-carbon-ecology coupling model to predict the results of multi-scenario coupling and coordination.

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