Land utilization-water quality-plankton coupled urban internal lake partition evaluation method

Abstract

According to the land utilization-water quality-plankton coupled urban internal lake zoning evaluation method provided by the invention, the lake is zoned based on special land utilization type and water quality index coupling, and zoning evaluation is carried out on each area. According to the method, land utilization types and water quality physical and chemical parameters around a drainage basin are obtained through remote sensing, step-by-step progressive and preliminary clustering (CA) is carried out on the water quality parameters, Kriging interpolation prediction space modeling is further carried out on the basis of calculation of comprehensive water quality parameters (WQI values), and therefore qualitative and quantitative reasonable partitioning of urban internal lakes is achieved. And water quality and plankton indexes are screened for zoning evaluation according to zoning characteristics.

Description

Technical Field

[0001] This invention belongs to the field of environmental science and engineering, and in particular to a method for evaluating urban inland lake zoning that couples land use, water quality, and plankton. Background Technology

[0002] Urban lakes, as an important component of urban ecosystems, are increasingly affected by surrounding land use patterns due to their unique geographical location and long-term high levels of human activity. The intermingling of urban land use, agricultural land, and fragmented green spaces resulting from urbanization is causing their ecological integrity to be increasingly impacted by these patterns. Land use not only affects phytoplankton community structure but also the physicochemical properties of lake water. Therefore, exploring the impact of land use patterns on urban lakes is particularly important.

[0003] Domestic and international research has yielded a series of results on watershed water environment assessment, which typically involves evaluating entire lakes and comparing multiple lakes. However, it is unknown whether different watersheds of a single lake present the same assessment results. Often, it is assumed that a single lake will show the same results. It is rare to divide a lake into east-west halves or north-south halves, but these are all completely subjective divisions. There is still no objective division method, and at present, there is a gap in the relevant technology. Summary of the Invention

[0004] Addressing the urgent need for aquatic ecological health assessment in urban waterways in my country, existing assessment methods are incomplete and fail to consider the spatial heterogeneity of watersheds. This invention proposes a land-use-water-plankton coupled urban lake zoning assessment method. Based on the specific land use type and water quality index coupling, this method divides lakes into zones and evaluates each zone separately. The method uses remote sensing to acquire land use types and water quality physicochemical parameters around the watershed, performs progressively layered water quality parameter analysis, initial clustering (CA), and further uses Kriging interpolation prediction spatial modeling based on the calculated comprehensive water quality parameter (WQI value). This achieves rational qualitative and quantitative zoning of urban lakes and evaluates the coupled plankton within each zone.

[0005] This invention discloses a land use-water quality-plankton coupled zoning evaluation method for urban inland lakes. Based on their unique geographical location, this method uses remote sensing to acquire land use types and water quality physicochemical parameters around the watershed. It then performs a step-by-step progressive clustering (CA) of water quality parameters, followed by Kriging interpolation prediction and spatial modeling based on the calculated comprehensive water quality index (WQI value). This enables rational qualitative and quantitative zoning of urban inland lakes, providing a more accurate and comprehensive evaluation of the aquatic ecological environment, avoiding one-size-fits-all and overly simplistic evaluation methods. Furthermore, it offers a new perspective for lake management.

[0006] The technical solution of the present invention is as follows:

[0007] A method for evaluating urban inland lake zoning based on land use-water quality-plankton coupling includes the following steps:

[0008] S1. Sampling points are laid out in a grid pattern based on the water area, with a point-to-area ratio of 2:1.

[0009] S2. Use ArcGIS to extract land use raster data within a 300m buffer zone of the riverbank and classify the land use types into forest, grassland, farmland, construction land, water area and unused land.

[0010] S3. Collect water samples from each sampling point and measure water temperature, dissolved oxygen, pH value, conductivity, turbidity, chemical oxygen demand, total phosphorus, total nitrogen, and ammonium nitrogen;

[0011] S4. Perform spatial cluster analysis based on water quality parameters to obtain quantitative zoning results;

[0012] S5. Calculate the comprehensive water quality index for each sampling point and generate a spatial model using Kriging interpolation.

[0013] S6. Combine the results of qualitative land use zoning, quantitative water quality parameter zoning, and comprehensive water quality index spatial model to determine the final lake area boundary;

[0014] S7. Zone-based screening of water quality and plankton indicators: Through Pearson correlation analysis, based on the criteria of |r|≥0.3 and p<0.05, indicators that are significantly correlated with the dominant land use type are retained;

[0015] S8. Assigning points to land use types, the formula is as follows:

[0016] ,

[0017] Where L represents the physical habitat score. w represents the percentage of the i-th land use type. li The weights for the i-th land use type index are: grassland and forest have a weight of 1, farmland has a weight of 4.54, and construction land has a weight of 9.42.

[0018] S9. Assign scores to the screened water quality indicators using the following formula:

[0019] ,

[0020] Where C represents the score for physicochemical properties, c i Let w be the fraction of the i-th physical and chemical property. ci The weight of the i-th physicochemical property index is based on the "Surface Water Environmental Quality Standard".

[0021] S10. Assigning scores to plankton indicators using the entropy weight method, the formula is as follows:

[0022] ,

[0023] Where B represents the score for aquatic organisms, b i w is the normalized value of the i-th aquatic biological indicator. bi To determine the weight of the i-th aquatic biological indicator, calculate the Regional Composite Index (PCI) using the following formula:

[0024] ,

[0025] PCI is the regional composite index, W L It is habitat weight, W C It is the weight of physicochemical properties, W B It is a planktonic weight, and the health level is determined based on the PCI value.

[0026] In the above technical solution, the spatial clustering analysis in step S4 adopts the systematic clustering method. When the clustering results are inconsistent with the qualitative zoning of land use, the Kriging interpolation model of WQI is used as the zoning benchmark.

[0027] In the above technical solution, when the qualitative land use classification and the quantitative water quality parameter classification are consistent, the classification is carried out according to the classification results. Since both are based on the delineation of sample points, Kriging interpolation is used to verify the classification at the spatial boundary. If abrupt changes occur in the verification results, if the abrupt change is at the boundary and the boundary position is deviated, the classification is correct. If the abrupt change is inside the partition, the partition is refined and the abrupt change area is set as a sub-partition while keeping the overall structure of the partition unchanged. If the abrupt change is scattered and has no spatial structure, it is set as a local anomaly and removed, and the partition remains unchanged.

[0028] When qualitative land use classification and quantitative water quality parameter classification yield inconsistent results, three layers are prepared in GIS: land use, water quality parameter clustering, and Kriging interpolation map. When the overlap between the Kriging distribution map of WQI and the boundary of a certain zone exceeds 70%, the boundary of that zone is considered to have higher spatial rationality. If it is between the two, the boundary is manually corrected according to the abrupt change point and the gradient with the maximum.

[0029] In the above technical solution, the water quality index screening rule in step S7 is as follows: when the proportion of the dominant land use type in the zone is >50%, water quality indicators that are significantly related to it are retained, and the standard for significant correlation is |r|≥0.3 and p<0.05; otherwise, all water quality indicators are retained.

[0030] In the above technical solution, the weight allocation rule in step S10 is as follows:

[0031] If the land use type zoning result has only a single type, then the weights for habitat, physical and chemical organisms, and plankton are 0.3, 0.3, and 0.4, respectively.

[0032] If the land use type zoning results are rich, then the weights for habitat, physicochemical properties, and plankton are 0.5, 0.3, and 0.2, respectively.

[0033] In the above technical solution, the planktonic data is acquired by a microscopic fluorescence imaging intelligent analyzer, including phytoplankton algal cell density and zooplankton abundance.

[0034] In the above technical solution, the health level is divided as follows:

[0035] PCI∈(80,100]: Very healthy;

[0036] PCI∈(60,80]: Healthy;

[0037] PCI∈(40,60]: Sub-health;

[0038] PCI∈(20,40]: Unhealthy;

[0039] PCI∈(0,20]: Inferior state.

[0040] In the above technical solution, the WQI calculation formula in step S5 is:

[0041] ,

[0042] n is the total number of parameters included in the water quality assessment, C i It is the normalized value of parameter i, P i It is the weight of parameter i.

[0043] In the above technical solution, the land use data in step S2 comes from the Resource and Environmental Science Data Center (RESDC).

[0044] An electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the steps of the method according to any one of the preceding claims.

[0045] A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0046] Beneficial effects:

[0047] 1. Breakthrough in spatial heterogeneity assessment: By coupling qualitative land use zoning with quantitative water quality modeling (CA clustering + Kriging interpolation), objective spatial zoning of urban lakes is realized for the first time, solving the "one-size-fits-all" evaluation bias of traditional methods.

[0048] 2. Dynamic screening of multi-dimensional indicators: Based on correlation analysis (|r|≥0.3), specific water quality and plankton indicators for each zone are dynamically screened to avoid redundant data interference and improve evaluation accuracy.

[0049] 3. Adaptive weight adjustment: The weights of habitat, physicochemical properties, and biological properties are automatically allocated based on the complexity of land use type (single / mixed) to ensure that the evaluation system matches the regional ecological characteristics.

[0050] 4. High efficiency in engineering applications: Combining remote sensing data with standardized algorithms (entropy weight method), it enables rapid zoning evaluation of large-scale lakes. The case of Tongling West Lake verifies that the health difference between the east and west halves of the lake reaches 20% (PCI=36 for the west half and PCI=45 for the east half). Attached Figure Description

[0051] Figure 1 Flowchart for determining the zoning of urban lakes;

[0052] Figure 2 Flowchart for the evaluation of urban lake zones;

[0053] Figure 3 Land use map of Tongling West Lake area;

[0054] Figure 4 For spatial cluster analysis;

[0055] Figure 5 Model the spatial prediction space for Kriging interpolation. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. However, the following embodiments are only for explaining the present invention, and the scope of protection of the present invention should include all the contents of the claims. Moreover, through the description of the following embodiments, those skilled in the art can fully implement all the contents of the claims of the present invention.

[0057] Example:

[0058] The simplified implementation steps of the urban inland lake zoning evaluation method coupled with land use, water quality, and plankton proposed in this invention are as follows:

[0059] 1. Survey the water area and select sampling points based on GPS. According to the water area, lay out the sampling points in a grid pattern, with the number of sampling points calculated as area:number of sampling points = 2:1, to ensure that sampling points are collected from every direction of the water area.

[0060] 2. Using ArcGIS 10.3 software, extract land use raster data, analyze the land use types within a 300m buffer zone of the riverbank, and calculate the buffer area for each land use type;

[0061] 3. Collect water samples and determine the physicochemical parameters of water temperature (WT), dissolved oxygen (DO), pH value, conductivity (EC), transparency (SD), turbidity (Tur), chemical oxygen demand (CODmn), total phosphorus (TP), total nitrogen (TN), and ammonium nitrogen (NH3N);

[0062] 4. Spatial clustering was performed on the ten measured physicochemical parameters of the water body to observe the spatial and temporal similarity between the variables;

[0063] 5. Calculate the comprehensive water quality parameters for each sample plot;

[0064] 6. Use ArcGIS 10.3 software to perform Kriging interpolation on the comprehensive water quality parameters;

[0065] 7. Determine lake zoning by combining qualitative analysis of land use and quantitative analysis of water body physicochemical parameters;

[0066] 8. Screening of water body physicochemical indicators based on land use types in different zones;

[0067] 9. Plankton indicators were screened based on land use types and water physicochemical indicators in different zones;

[0068] 10. Assign scores to physical habitat land use types based on landscape development intensity;

[0069] 11. Assign scores to physicochemical properties based on water quality index measurements;

[0070] 12. Assign scores to plankton indicators based on plankton benchmarks combined with the entropy weight method;

[0071] 13. Obtain the comprehensive index and health level of urban inland lake zones.

[0072] The detailed implementation steps and descriptions of this invention are as follows:

[0073] Due to their unique geographical location, urban lakes are increasingly affected by the intertwined distribution of building land, agricultural land, and fragmented green spaces brought about by urbanization, making their ecological integrity increasingly susceptible to the influence of surrounding land use types. This study selected lakes within the city, used GPS to determine latitude and longitude, and then laid out monitoring points in a grid pattern based on the water area, with the number of points set at a ratio of 2:1 (area:number of points) to each location.

[0074] Land use data provided by Resources and Environmental Sciences (https: / / www.resdc.cn / Default.aspx) was used to extract land use raster data using ArcGIS 10.3 software. Land use types within a 300m buffer zone along the riverbank were analyzed, and the original land use (LU) categories were further divided into five classes: Forest, Grassland, Cropland, Construction, and Water. The buffer area for each land use type in each sample plot was calculated. Figure 3 As shown.

[0075] Based on the land use type of each sample plot, all points are simply divided into zones (e.g., each type is one type; or one type is one type, and the other two types are equal in number and constitute the second type; if there are not enough, two or more types constitute the second type; and so on).

[0076] Water samples were collected from each sampling site. Water temperature (WT), dissolved oxygen (DO), pH, conductivity (EC), and turbidity (Tur) were measured in situ using a YSI multi-parameter water quality analyzer (Thermo Fisher Scientific, Waltham, MA, USA). Secchi transparency (SD) was measured using a Secchi disc. In the laboratory, ammonia nitrogen (NH3-N, HJ 195-2023), permanganate index (CODmn, GB / T 11892-1989), chemical oxygen demand (COD, HJ / T 399-2007), total nitrogen (TN, HJ 199-2023), and total phosphorus (TP, GB / T 11893-1989) were measured. All measured indices were compiled and recorded.

[0077] Spatial clustering (CA) is a multivariate statistical method. Using IBM SPSS Statistics 26, hierarchical clustering is employed to cluster water quality physicochemical data from all monitoring points, allowing observation of spatial and temporal similarities between variables. The data is then partitioned according to the clustering results, such as... Figure 4 As shown.

[0078] The comprehensive water quality index (WQI) was calculated for each sample plot. WQI reflects the comprehensive evaluation result of different water quality parameters. WQI ranges from 0 to 100, with higher values ​​indicating better water quality. Based on the WQI values, Kriging interpolation was performed using ArcGIS 10.3 software. Quantitative zoning was then performed based on the interpolation results, such as... Figure 5 As shown.

[0079] ,

[0080] n is the total number of parameters included in the water quality assessment, C i It is the normalized value of parameter i, P i This refers to the weight of parameter i. The WQI is weighted and scored according to the "Surface Water Environmental Quality Standard (China)".

[0081] In step 5, the spatial clustering results are partitioned based on similarity, and the qualitative partitioning of land types is compared. If they differ, Kriging interpolation prediction analysis is performed. Spatial clustering is point partitioning, while Kriging interpolation is area partitioning. The final partitioning combines qualitative and quantitative comprehensive partitioning, such as... Figure 1 As shown.

[0082] Based on the zoning results, phytoplankton water samples from each region were analyzed using the Phytoplankton Micro-fluorescence Imaging Intelligent Analyzer (AIA) and Zooplankton Micro-imaging Intelligent Analyzer (ZIA), both independently developed by the Anhui Institute of Optics, Fine Mechanics and Phytoplankton Research Institute, Hefei Institutes of Physical Science, Chinese Academy of Sciences, to identify algal cells and zooplankton and obtain their biological indicators.

[0083] Based on the zoning results in step 6, water body physicochemical indicators are screened based on land use types, and the adaptability between land use types and water quality physicochemical properties is verified. This adaptability verification mainly involves calculating the percentage of major land use types in each region after zoning, extracting types with higher percentage importance (excluding water bodies), and performing Pearson correlation analysis. If the correlation |r| ≥ 0.3 and p < 0.05 between two indicators, the physicochemical property indicators are retained.

[0084] Based on the physicochemical indicators selected in step 8, repeat step 8, couple the percentage of land use type with the physicochemical indicators, and retain the aquatic biological indicators through correlation analysis.

[0085] Scoring is based on landscape development intensity and physical habitat land use type, using the following formula:

[0086] ,

[0087] Where L represents the physical habitat score. w represents the percentage of the i-th land use type. li denoted as the weight of the i-th land use type indicator; among which, grassland, water, forest and unused land have a weight of 1, agriculture has a weight of 4.54, urban and building land has a weight of 9.42, and rural land has a weight of 8.66.

[0088] Based on the selected and retained physicochemical properties, the physicochemical properties are weighted and scored according to the "Surface Water Environmental Quality Standard (China)" using the following formula:

[0089] ,

[0090] Where C represents the score for physicochemical properties, c i Let w be the fraction of the i-th physical and chemical property. ci is the weight of the i-th physicochemical property index.

[0091] Based on the planktonic baseline, the retained planktonic indicators were normalized using IBM SPSS Statistics 26, and the entropy weight method was used to assign weights to the planktonic indicators. The formula is as follows:

[0092] ,

[0093] Where B represents the score for aquatic organisms, b i w is the normalized value of the i-th aquatic biological indicator. bi Let be the weight of the i-th aquatic biological indicator.

[0094] The formula for calculating the comprehensive index of urban lakes is as follows:

[0095] ,

[0096] PCI is the regional composite index, W L It is habitat weight, W C It is the weight of physicochemical properties, W B It is the weight of plankton.

[0097] If the land use type zoning result is only a single type, it indicates that the impact of this land use type on the physical and chemical properties of water bodies and plankton is relatively weak. In this case, the weights for habitat, physical and chemical properties, and plankton are 0.3, 0.3, and 0.4, respectively.

[0098] If the land use type zoning results are rich at this time, it means that the land use type evenly affects the water quality physicochemical properties and plankton, while the plankton index is affected by land use and water quality physicochemical properties. At this time, the weights for habitat, physicochemical properties, and plankton are 0.5, 0.3, and 0.2, respectively.

[0099] Based on the comprehensive index of urban lake zones calculated in the above steps, the zone evaluation is divided into five levels: very healthy, healthy, sub-healthy, unhealthy, and poor. Figure 2 As shown.

[0100] This invention discloses a land use-water quality-plankton coupled zoning evaluation method for urban inland lakes. Based on their unique geographical location, this method uses remote sensing to acquire land use types and water quality physicochemical parameters around the watershed. It then performs progressively layered water quality parameters, initial clustering (CA), and further uses Kriging interpolation prediction spatial modeling based on the calculated comprehensive water quality parameters (WQI value). This enables rational qualitative and quantitative zoning of urban inland lakes, allowing for a more accurate evaluation of the aquatic ecological environment. This invention selected the Tongling West Lake wetland area for temporal zoning evaluation, as shown in the zoning table below.

[0101] Table 1. Qualitative Land Use Zoning Table for Tongling West Lake

[0102]

[0103] 1. The land use type within a 300-meter buffer zone around the Tongling West Lake Wetland is classified into two categories based on land use type: building land and agricultural land in the western half of the lake; and agricultural land only in the eastern half of the lake.

[0104] 2. The spatial clustering analysis yielded zoning results consistent with the qualitative analysis of land use.

[0105] 3. Calculate the comprehensive water quality parameters (WQI value) and perform spatial modeling for Kriging interpolation prediction.

[0106] 4. Based on the aforementioned zoning method combining qualitative and quantitative land use analysis, the Tongling West Lake area is divided into a western half and an eastern half. Indicators were screened for each half. For the eastern half, building land and agricultural land were standardized with physicochemical indicators using IBM SPSS Statistics 26, followed by Pearson correlation analysis. For the 10 water quality physicochemical indicators, the correlation coefficients |r| ≥ 0.3 and p < 0.05 between building and agriculture were found, ultimately retaining DO, pH, COD, TN, TP, and Tur. Similarly, for the western half, only agricultural land retained TN and TP. The same approach was applied to plankton.

[0107] 5. Scores were assigned to the three dimensions of habitat, physicochemical properties, and aquatic organism screening. The total score for the western half of the lake was 36 points, and for the eastern half of the lake it was 45 points. In this embodiment, scores in (80, 100) were set as excellent, scores in (60, 80) as good, scores in (40, 60) as average, scores in (20, 40) as poor, and scores in (0, 20) as inferior.

[0108] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A land-use-water-plankton coupled urban lake zoning evaluation method, characterized in that, Includes the following steps: S1. Sampling points are laid out in a grid pattern based on the water area, with a point-to-area ratio of 2:

1. S2. Use ArcGIS to extract land use raster data within a 300m buffer zone of the riverbank and classify the land use types into forest, grassland, farmland, construction land, water area and unused land. S3. Collect water samples from each sampling point and measure water temperature, dissolved oxygen, pH value, conductivity, turbidity, chemical oxygen demand, total phosphorus, total nitrogen, and ammonium nitrogen; S4. Perform spatial cluster analysis based on water quality parameters to obtain quantitative zoning results; S5. Calculate the comprehensive water quality index (WQI) for each sampling point and generate a spatial model using Kriging interpolation. S6. Combine the results of qualitative land use zoning, quantitative water quality parameter zoning, and comprehensive water quality index spatial model to determine the final lake area boundary; S7. Zone-based screening of water quality and plankton indicators: Through Pearson correlation analysis, based on the criteria of |r|≥0.3 and p<0.05, indicators that are significantly correlated with the dominant land use type are retained; S8. Assigning points to land use types, the formula is as follows: ， Where L represents the physical habitat score. w represents the percentage of the i-th land use type. li The weights for the i-th land use type index are: grassland and forest have a weight of 1, farmland has a weight of 4.54, and construction land has a weight of 9.

42. This is based on existing research findings on the impact of land use on water quality. S9. Assign scores to the screened water quality indicators using the following formula: ， Where C represents the score for physicochemical properties, c i Let w be the fraction of the i-th physical and chemical property. ci The weight of the i-th physicochemical property index is based on the "Surface Water Environmental Quality Standard". S10. Assigning scores to plankton indicators using the entropy weight method, the formula is as follows: ， Where B represents the score for aquatic organisms, b i w is the normalized value of the i-th aquatic biological indicator. bi To determine the weight of the i-th aquatic biological indicator, calculate the zoning composite index (PCI) using the following formula: ， PCI is the regional composite index, W L It is habitat weight, W C It is the weight of physicochemical properties, W B It is a planktonic weight, and the health level is determined based on the PCI value.

2. The method according to claim 1, characterized in that, In step S4, spatial cluster analysis uses hierarchical clustering.

3. The method according to claim 1, characterized in that, The final lake area boundary delineation rules in step S6 are as follows: When qualitative land use zoning and quantitative water quality parameter zoning are consistent, the zoning is carried out according to the zoning results. Since both are based on the delineation of sample points, Kriging interpolation is used to verify the zoning at the spatial boundaries. If abrupt changes occur in the verification results, if the abrupt change is at the boundary and the boundary position is deviated, the zoning is correct. If the abrupt change is inside the zoning, the zoning is refined and the abrupt change area is set as a sub-zoning while keeping the overall structure of the zoning unchanged. If the abrupt change is scattered and has no spatial structure, it is set as a local anomaly and removed, and the zoning remains unchanged. When qualitative land use classification and quantitative water quality parameter classification yield inconsistent results, three layers are prepared in GIS: land use, water quality parameter clustering, and Kriging interpolation map. When the overlap between the Kriging distribution map of WQI and the boundary of a certain zone exceeds 70%, the boundary of that zone is considered to have higher spatial rationality. If it is between the two, the boundary is manually corrected according to the abrupt change point.

4. The method according to claim 1, characterized in that, The water quality indicator screening rules in step S7 are as follows: when the proportion of the dominant land use type in the zone is >50%, water quality indicators that are significantly related to it are retained. The standard for significant correlation is |r|≥0.3 and p<0.05; otherwise, all water quality indicators are retained.

5. The method according to claim 1, characterized in that, The weight allocation rule in step S10 is as follows: If the land use type zoning result has only a single type, then the weights for habitat, physical and chemical organisms, and plankton are 0.3, 0.3, and 0.4, respectively. If the land use type zoning results are rich, then the weights for habitat, physicochemical properties, and plankton are 0.5, 0.3, and 0.2, respectively.

6. The method according to claim 1, characterized in that, The planktonic data was acquired using a microscopic fluorescence imaging intelligent analyzer, including phytoplankton cell density and zooplankton abundance.

7. The method according to claim 1, characterized in that, Health levels are classified as follows: PCI∈(80,100]: Very healthy; PCI∈(60,80]: Healthy; PCI∈(40,60]: Sub-health; PCI∈(20,40]: Unhealthy; PCI∈(0,20]: Inferior state.

8. The method according to claim 1, characterized in that, The WQI calculation formula in step S5 is: ， n is the total number of parameters included in the water quality assessment, C i It is the normalized value of parameter i, P i It is the weight of parameter i.

9. The method according to claim 1, characterized in that, In step S2, the land use data is sourced from the Resource and Environmental Science Data Center (RESDC).

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the program, it implements the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The device stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1-8.

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