A method for predicting spatial distribution of groundwater recovery force and uncertainty thereof

Abstract

The present application belongs to the technical field of hydrogeology, and discloses a prediction method for spatial distribution of groundwater recovery force and uncertainty thereof, comprising: calculating groundwater recovery force by using groundwater performance index and recovery force index; and quantitatively analyzing the relationship between the groundwater recovery force and nine kinds of groundwater control factors, i.e., lithology, topography, slope, distance to river, soil, canal system density, permeability coefficient, groundwater depth and land use, based on GIS technology combined with DS model to generate a groundwater recovery force zoning map. The present application can reliably predict the spatial distribution of groundwater recovery force. The present application can not only output the support degree of the evidence layer to the prediction result through the belief function, but also can quantify the uncertainty of the prediction result to reflect systematic error and random error, so that the generated spatial prediction result is accurate and reliable.

Description

Technical Field

[0001] This invention belongs to the field of hydrogeology, and in particular relates to a method for predicting the spatial distribution and uncertainty of groundwater resilience. Background Technology

[0002] Currently, accurately quantifying the spatial distribution of groundwater resilience is a challenging problem in the field of hydrogeology. The Hotan River Basin is a typical arid inland river basin in Northwest China, where groundwater resources play a crucial role in the region's sustainable development. Understanding the spatial distribution of groundwater resilience in this area is key to the rational utilization and effective management of groundwater resources.

[0003] Groundwater resources are vital natural resources, and changes in groundwater storage are crucial for sustainable water resource management. Under the dual pressures of climate change and human activities, groundwater resources in arid inland river basins are already overburdened and insufficient to meet demand. However, this important resource faces serious risks, including declining groundwater levels, soil salinization, desertification, and vegetation degradation, further deteriorating fragile ecosystems. In arid regions where water resource development and utilization have approached or exceeded their limits, water security faces severe challenges, and the resilience of groundwater resource systems in arid areas has become a major concern.

[0004] The Dempster-Shafer (DS) theoretical model supports the fusion of multi-source heterogeneous information and can flexibly construct the basic probability allocation (BPA) of single-factor indicators of groundwater resilience and spatial data layers of resilience influencing factors. It can also provide prediction results, systematic errors and random errors at the same time, making it a potentially reliable method for predicting the spatial distribution of groundwater resilience.

[0005] Existing methods for predicting the spatial distribution of groundwater resilience mainly employ single-factor evaluation or index system evaluation. However, single-factor evaluation results are inconsistent due to different selected indicators and calculation methods. While index system evaluation considers multiple influencing factors, it can only obtain the average level of resilience at the regional scale, making it difficult to obtain the spatiotemporal distribution characteristics of resilience. Furthermore, the reliability of the weight selection for each indicator is difficult to verify.

[0006] Based on the above analysis, the problems and defects of the existing technology are as follows: the existing methods for predicting the spatial distribution of groundwater resilience result in inconsistencies due to different selected indicators and calculation methods; or they can only obtain the average level of resilience at the regional scale, making it difficult to obtain the spatial distribution characteristics of resilience, and the reliability of the selection of the weights of each indicator is also difficult to verify. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for predicting the spatial distribution and uncertainty of groundwater resilience.

[0008] This invention is implemented as follows: a method for predicting the spatial distribution and uncertainty of groundwater resilience, wherein the method includes:

[0009] Groundwater resilience is calculated using groundwater performance indicators and resilience index; the relationship between groundwater resilience and nine groundwater control factors is quantitatively analyzed based on GIS technology and the DS evidence conflict theory model, and a groundwater resilience zoning map is generated.

[0010] Furthermore, the nine groundwater control factors include: lithology, topography, slope, distance to the river, soil, canal density, permeability coefficient, groundwater depth, and land use.

[0011] Furthermore, the method for predicting the spatial distribution and uncertainty of groundwater resilience also includes:

[0012] Based on two indicators, groundwater performance index and resilience index, DS models are constructed to predict the spatial resilience of groundwater.

[0013] Nine groundwater control factors were selected and a spatial dataset was created as the evidence layer in the DS model. The fusion results of the DS model were used to spatially predict groundwater resilience and output a groundwater resilience zoning map.

[0014] Furthermore, the method for predicting the spatial distribution and uncertainty of groundwater resilience includes the following steps:

[0015] Step 1: Obtain monthly groundwater level data, calculate groundwater performance indicators and resilience index, and determine groundwater resilience.

[0016] Step 2: Select 9 groundwater control factors as evidence layers to construct the DS model, and establish mathematical relationships between groundwater performance indicators and resilience index and groundwater control factors respectively.

[0017] Step 3: Generate a spatial distribution map of groundwater resilience in a GIS environment based on evidence fusion.

[0018] Furthermore, the calculation of groundwater performance indicators and resilience index, and the determination of groundwater resilience, respectively, includes:

[0019] First, calculate the groundwater performance indicators using the following formula:

[0020] but ;

[0021] otherwise ;

[0022] ;

[0023] in, Indicates groundwater performance indicators; This represents the multi-year average groundwater level. Represents the time series of groundwater levels in wells; satisfactory state This indicates that the groundwater level in the well has reached the multi-year average, which is an unsatisfactory state. This indicates that the groundwater level at the wellhead is lower than the multi-year average.

[0024] Secondly, the restoring force of the groundwater system at time t is calculated using the following formula:

[0025] ;

[0026]

[0027] =0.375 ;

[0028] ;

[0029] in, Indicates resilience index; Represents state variables exist Time and Proximity ( The threshold for the difference; and These represent the maximum and minimum values ​​of the monthly groundwater level data sequence, respectively.

[0030] Furthermore, in step two, nine groundwater control factors are selected as evidence layers to construct the DS model, and the mathematical relationships between groundwater performance indicators and resilience indices and groundwater control factors are established, including:

[0031] A raster dataset of nine groundwater control factors was generated in a GIS environment, and the following formula was used based on groundwater performance indicators. and resilience index The calculated belief function components of the groundwater resilience control factors for each category clearly define the relationship between groundwater resilience and the nine control factors:

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] in, Indicates the first The first layer of spatial information Class attributes, This indicates support for the hypothesis. The likelihood ratio, To show support for the hypothesis The likelihood ratio is calculated using the following formula:

[0037] ;

[0038] ;

[0039] in, The middle supports the hypothesis The number of wells; This indicates that the hypothesis is supported across the entire region. The total number of wellheads; Indicates the first The first in the layer The total number of grid cells for class attributes; This represents the total number of grid cells in the entire area.

[0040] Furthermore, in step three, generating a spatial distribution map of groundwater resilience in a GIS environment based on evidence fusion includes:

[0041] 1) Determine the basic probability allocation for each information layer based on the spatial location of the observation wells and the spatial relationship between the control factor layers of groundwater;

[0042] 2) Overlay the attributes of the groundwater control factor layers to obtain raster layers with confidence, rejection, likelihood, and uncertainty for each groundwater control factor.

[0043] 3) Based on the evidence fusion rules, BPA fusion of different information layers is performed through eight iterations of the evidence layer, and the evidence fusion result is output. The groundwater resilience zoning map is generated using the evidence fusion result.

[0044] Furthermore, the evidence fusion rules are as follows:

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] in, Indicated by evidence The level of trust generated Indicated by evidence The resulting likelihood Indicated by evidence The resulting uncertainty is calculated as follows: , Indicated by evidence The resulting level of distrust is calculated as follows: ; This indicates a low confidence level for each factor type or range. This indicates the level of distrust for each factor type or range. This indicates the uncertainty of the type or range of factors at each level. This indicates the 1st, 2nd, ..., 9th factor type. This is the normalization factor.

[0051] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for predicting the spatial distribution and uncertainty of groundwater resilience.

[0052] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for predicting the spatial distribution and uncertainty of groundwater resilience.

[0053] Based on the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by this invention from the following aspects:

[0054] First, addressing the technical problems existing in the prior art and the difficulty in solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0055] This invention can reliably predict the spatial distribution of groundwater resilience. It not only outputs the degree of support for the prediction results through a belief function, but also quantifies the uncertainty of the prediction results to reflect systematic and random errors, generating accurate and reliable spatial prediction results.

[0056] Second, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0057] This invention provides some assistance in understanding the resilience of groundwater in arid inland river basins, and the results may provide a scientific basis for relevant departments in Hotan Prefecture to plan and manage groundwater resources. The prediction method based on the spatial distribution and uncertainty of groundwater resilience in this invention is rapid and effective for predicting regional groundwater resilience, and can provide a scientific basis for groundwater safety and scientific management. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the principle of the method for predicting the spatial distribution and uncertainty of groundwater resilience provided in an embodiment of the present invention.

[0059] Figure 2 This is a flowchart of a method for predicting the spatial distribution and uncertainty of groundwater resilience provided in an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram illustrating the relationship between evidence belief functions provided in the embodiments of the present invention;

[0061] Figure 4 This is based on groundwater performance indicators provided in the embodiments of the present invention. and resilience index A diagram illustrating the proportions of different levels of resilience;

[0062] Figure 5 (a) is a schematic diagram of the integration results of the Dempster-Shafer theoretical model based on the resilience index provided in the embodiments of the present invention; (b) is a schematic diagram of the confidence level provided in the embodiments of the present invention; (c) is a schematic diagram of the rejection level provided in the embodiments of the present invention; (d) is a schematic diagram of the uncertainty level provided in the embodiments of the present invention;

[0063] Figure 6 This is a map of the groundwater resilience potential zone in the study area based on the Dempster-Shafer theoretical model, provided in an embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0065] I. Explanatory and Illustrative Embodiments. To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory and illustrative description of the embodiments described in the claims.

[0066] like Figures 1-2 As shown, the method for predicting the spatial distribution and uncertainty of groundwater resilience provided in this embodiment of the invention includes the following steps:

[0067] S101, Obtain monthly groundwater level data, calculate groundwater performance indicators and resilience index respectively, and determine groundwater resilience;

[0068] S102, nine groundwater control factors were selected as evidence layers to construct the DS model, and mathematical relationships between groundwater performance index and resilience index and groundwater control factors were established respectively.

[0069] S103, Generating a spatial distribution map of groundwater resilience in a GIS environment based on evidence fusion.

[0070] The method for predicting the spatial distribution and uncertainty of groundwater resilience provided in this invention specifically includes:

[0071] 1. Groundwater resilience index

[0072] Groundwater performance indicators ( This represents the speed at which the system recovers from an unsatisfactory state to a satisfactory state. It is an index reflecting the autocorrelation of variables within a given time period, based on convex models and critical deceleration theory. This method can not only quantify the time-varying resilience of a system, but also predict the critical transition of the system's resilience. This invention intends to use both of these methods to calculate the resilience of groundwater.

[0073] 1.1 Groundwater performance indicators ( )

[0074] Using groundwater performance indicators in traditional RRV standards To evaluate the groundwater resilience of the study area's watershed, a standard is first defined for each groundwater well. (i.e., the multi-year average groundwater level) Represents the time series of groundwater levels in wells, in a satisfactory state. This indicates that the groundwater level in the well has reached the multi-year average, which is an unsatisfactory state. This indicates that the groundwater level at the wellhead is lower than the multi-year average, and the groundwater performance index is defined as shown in equation (1):

[0075] but ;

[0076] otherwise ;

[0077]

[0078] In this invention It was used to assess the resilience of wells in the study area, ranging from 0 to 1. This index reflects the speed at which the system recovers to a satisfactory state after a system failure. The higher the value, the higher the resilience.

[0079] 1.2 Resilience Index (

[0080] This invention uses groundwater level as a state variable and utilizes... This method quantifies the temporal variation of groundwater resilience in the study area. Groundwater resilience can be measured through state variables. exist Time and neighboring points ( The magnitude of the difference (where the number of adjacent points is 2n) reflects the resilience of the groundwater system. A larger difference indicates a lower degree of autocorrelation in the time series corresponding to the state variable, and a higher resilience of the groundwater system. The calculation method for ) is shown in formulas (2)-(5):

[0081] (2)

[0082] (3)

[0083] =0.375 (4)

[0084] (5)

[0085] in It is a state variable The threshold for difference from neighboring points. and These represent the maximum and minimum values ​​of the monthly groundwater level data sequence, respectively. Take an empirical value of 0.75 as the threshold. It is 0.375 (Formula (4)). The time variation is consistent for different n. In this invention, n is taken as 4 (the number of adjacent points is 8). The value range is 0-8. Based on the groundwater level data, the groundwater system in the study area can be calculated using formulas (2)-(5). The ability to recover in time.

[0086] 2. Groundwater resilience zoning (Groundwater resilience zoning map)

[0087] The method may mainly include the following steps:

[0088] (1) Create a spatial dataset of groundwater control factors as evidence dataset for the DS model;

[0089] (2) Establish the mathematical relationship between resilience and groundwater control factors based on the DS model and achieve evidence fusion;

[0090] (3) Create and verify the groundwater resilience zoning map.

[0091] (4) Explanation and comparison of results.

[0092] 2.1 Groundwater Restoration Capacity Moderating Factors

[0093] To establish a more reliable and accurate groundwater resilience prediction model for the study area, a spatial database with different spatial datasets needs to be created. The spatial distribution of groundwater is influenced by natural factors such as meteorology, hydrology, geomorphology, and geological structure, as well as anthropogenic factors such as land use. This invention considers nine groundwater control factors, including lithology, topography, slope, distance to rivers, soil, canal density, permeability coefficient, groundwater depth, and land use. These factors affect the groundwater resilience of the region by influencing its storage, recharge, migration, and discharge. A raster dataset of the nine groundwater control factors is generated in a GIS environment and will be used to evaluate the groundwater resilience of the study area.

[0094] 2.2 Dempster-Shafer (DS) Evidence Theory Model

[0095] In this invention, the DS evidence theory model is used to predict the groundwater resilience in the study area. The DS evidence theory model is constructed through steps including establishing an identification framework, determining basic probability assignments, calculating the quality function, and achieving evidence fusion. Identification Framework It is the set of all possible hypotheses for judging a certain object, and is a mutually exclusive, non-empty finite set (Equation 6). In Equation 6 For each grid Regarding the possibility of high groundwater resilience in the target area For the opposite assumption, that is, each grid The possibility of low groundwater resilience. (Regarding the identification framework) The assumptions in the equation are used to perform a basic probability assignment (BPA), and the basic probability function is... (Formula 7) needs to meet the following conditions. and .

[0096] (6)

[0097] (7)

[0098] Based on existing calculations of groundwater resilience, respectively using ≥0.094 ≥2 as The discrimination target. The trust function degree and rejection degree are calculated using formula 8-11. Likelihood and Uncertainty ). It indicates the degree of trust in any given hypothesis. The calculations include the degree of trust in the hypothesis and the degree of trust in the unknown domain. and The difference between them is expressed as Trust function and likelihood function They represent the hypotheses respectively. The upper and lower limits of trust levels. The interval is called the hypothesis. The reliability interval, representing the range of uncertainty. Figure 4 The size of the confidence interval determines the level of credibility of the evidence. The larger the interval, the lower the credibility of the evidence, and vice versa.

[0099] (8)

[0100] (9)

[0101] (10)

[0102] (11)

[0103] in, For the first The first layer of spatial information Class attributes, To support the hypothesis The likelihood ratio, To support the hypothesis The likelihood ratio. Its calculation formula is:

[0104] (12)

[0105] (13)

[0106] In the formula: for The middle supports the hypothesis The number of wells; To support the hypothesis across the entire region The total number of wellheads; For the first The first in the layer The total number of grid cells for class attributes; This represents the total number of grid cells in the entire area.

[0107] Based on the spatial location of the observation wells and the spatial relationship between the groundwater control factor layers, the basic probability assignment (BPA) for each information layer is determined. The attributes of the groundwater control factor layers are superimposed to obtain raster layers representing the confidence, rejection, likelihood, and uncertainty of each groundwater control factor. According to the fusion rules of the DS evidence theory, the BPAs of different information layers are fused to obtain the final result. The evidence fusion rules used in this invention are as follows:

[0108] (14)

[0109] (15)

[0110] (16)

[0111] (17)

[0112] (18)

[0113] Based on evidence The resulting level of trust is used This indicates that the likelihood is used Uncertainty is expressed as... It means that the calculation is as follows: Distrust level It means that the calculation is as follows: . In formula 14-18: This indicates a low confidence level for each factor type or range. This indicates the level of distrust for each factor type or range. This indicates the uncertainty of the type or range of factors at each level. This indicates the 1st, 2nd, ..., 9th factor type. It is called the normalization factor.

[0114] The quality function in the DS evidence theory model includes confidence, rejection, uncertainty, and likelihood, which are the belief function components (belief functions) evaluating the predictive ability of the evidence model. Using formulas 14-18, the evidence layer is iterated. Considering nine factors as evidence layers, eight iterations are required. The final evidence fusion result is used to construct a groundwater resilience zoning map of the study area.

[0115] II. Application Examples. To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides application examples of the technical solution of the claims on specific products or related technologies.

[0116] The method for predicting the spatial distribution and uncertainty of groundwater resilience provided in this embodiment of the invention is applied to the Hotan watershed for predicting the spatial distribution of groundwater resilience.

[0117] III. Evidence of the Relevant Effects of the Embodiments. The embodiments of the present invention have achieved some positive effects during research and development or use, and indeed possess significant advantages compared to existing technologies. The following description, in conjunction with data, charts, and other materials from the experimental process, illustrates these advantages.

[0118] 1.1 Groundwater resilience based on groundwater performance indicators and resilience index

[0119] Based on groundwater level data in the study area, this invention utilizes the resilience index. and groundwater performance indicators Two methods were used to quantitatively estimate groundwater resilience, and the maximum, minimum, average, standard deviation, and coefficient of variation for each indicator were statistically analyzed. (The remaining text appears to be incomplete and requires further context.) and Based on this classification, resilience is divided into five levels. The proportion of each index at each level is calculated to evaluate the groundwater resilience of each well, and the spatial distribution pattern is statistically analyzed. In this invention, using... Based on this classification, 19% of the wells had low resilience (compared to moderate resilience), while 44% of the wells had high resilience; Based on the classification criteria, 74% of the wells have high groundwater resilience; areas with relatively high resilience are mainly distributed in the upstream oasis irrigation area and near the downstream river channel, while areas with relatively low resilience are mainly distributed in areas with greater groundwater depth and farther away from the river channel.

[0120] 1.2 Application of the Dempster-Shafer theoretical model in groundwater resilience mapping

[0121] Based on groundwater performance indicators (≥0.094) and resilience index The belief function components (including Bel, Dis, Unc, Pls) of the groundwater resilience control factors obtained by the formula (8)-(13) for wells with (≥2) can fully reflect the relationship between groundwater resilience and the nine control factors.

[0122] The Bel value directly reflects the level of groundwater resilience; a relatively high Bel value indicates higher groundwater resilience. A Bel value of 0 in a particular category indicates the absence of groundwater wells in that category. A higher Bel value represents a greater likelihood of groundwater recovery within that category. In this invention, the Bel values ​​for fine sand and gravel are higher than other lithological units, indicating a positive correlation between this category and groundwater resilience. For soil types, anthropogenic soils and semi-aqueous soils have the highest Bel values, indicating the highest groundwater resilience, while the relationship between desert soils, primary terranean soils, and saline-alkali soils and groundwater resilience is weak.

[0123] The components of the integrated belief function (Bel, Dis, Unc, Pls) after fusing the nine evidence layers will be used to generate a spatial distribution map. The spatial distribution of Bel can explain the combination of groundwater resilience and the evidence layers of the nine control factors; there is a correlation between areas with high Bel values, low Dis values, high Pls values, and low Unc values. The spatial distribution of Bel is opposite to that of Dis; in areas with high Unc, Bel values ​​are always low, indicating that the belief in high groundwater resilience is reliable. In areas with high Pls values, Bel and Unc values ​​are also relatively high. The belief function map combining the two indicators can reveal the spatial pattern of groundwater resilience distribution. In this invention, the areas with the highest groundwater resilience in the study area are mainly located in the oasis irrigation area in the southern part of the study area, where the river channel and canal system density are relatively high, the terrain is relatively flat, and the lithological units are mostly fine sand and gravel. Areas with relatively low groundwater resilience are mainly concentrated around the oasis irrigation area and in the middle area between the river channel and the desert.

[0124] 1.3 Verification of Groundwater Restoration Capacity Map

[0125] In a GIS environment, the confidence map is overlaid with the locations of 27 wells to obtain the predicted values ​​for each point. Then, the values ​​of the 27 wells are... and The groundwater resilience of the well is divided into five levels: extremely low, low, medium, relatively high, and high. The groundwater resilience of the well is compared with the predicted value at that location, and the accuracy of each model is calculated. and The DS model using nine evidence layers for groundwater control factors achieved accuracies of 40.7% and 59.3%, respectively. Subsequently, unimportant groundwater control factors were removed from the model, and the model was run again. Therefore, it was considered to remove relatively coarse and unvalidated data from the evidence layers. The final model results were output after removing some evidence layers. The DS model's prediction accuracy increased by 7.4%, based on The accuracy of the model increased by 11.2%. Based on actual calculation results, the reliability of the two models for evaluating groundwater resilience was compared.

[0126] The prediction and validation results of the DS model show that its understanding of the spatial distribution of groundwater resilience in the Hotan River Basin is basically consistent with the hydrogeological conditions and groundwater cycle patterns of the basin, and is consistent with the resilience index calculated by observation wells, thus reliably predicting the spatial distribution of groundwater resilience. However, the number and uniformity of the distribution of groundwater observation wells, as well as the scale of the spatial evidence layer calculation grid, are important factors affecting the accuracy of the model and areas that require further improvement in this invention.

[0127] 1.4 Creating a zoning map of groundwater resilience

[0128] The groundwater resilience zoning map, generated using Bel values, shows different ranges of groundwater resilience within the study area, representing the degree of support for groundwater resilience from the evidence layer, and is considered the most likely groundwater resilience prediction map. Based on The DS model explains the eight evidence layers (excluding lithology) and Based on the spatial relationship between them, Bel values ​​are divided into five categories: very low (Bel 0-0.072, accounting for 3% of the total area), low (0.072-0.171, 30%), medium (0.171-0.458, 30%), relatively high (0.458-0.786, 22%), and high (>0.786, 15%). The DS model explains the seven evidence layers (excluding lithology and horizontal permeability) and The spatial relationships between the regions were analyzed, with Bel values ​​categorized as extremely low (0-0.168), low (0.168-0.260), medium (0.260-0.405), relatively high (0.405-0.632), and high (>0.632). Regions with Bel values ​​ranging from extremely low to high accounted for 25%, 15%, 21%, 22%, and 17% of the total area, respectively. Based on the prediction maps using the two resilience indices, it was found that regions with relatively high and high resilience exhibited good consistency, primarily distributed in the upstream oasis irrigation area and around the downstream river channel.

[0129] 2. Summary

[0130] This invention aims to address the issue based on groundwater performance indicators. and resilience index Two separate DS models were constructed to spatially predict groundwater resilience in the study area using two indicators, and the effectiveness of the two indicators in predicting groundwater resilience was evaluated. Nine groundwater control factors were selected and a spatial dataset was created as the evidence layer in the DS model. The fusion results of the combined DS models were used to spatially predict groundwater resilience in the study area, and a groundwater resilience zoning map was finally output.

[0131] As a quantitative standard for groundwater resilience and Both methods can address the long-term changes in groundwater system resilience. Wells with relatively high resilience are mainly distributed in upstream oasis irrigation areas and near downstream river channels, while wells with relatively low resilience are mainly distributed in areas with greater underground depth and farther from river channels. A DS model was constructed based on an evidence layer composed of two resilience indices and groundwater control factors to obtain groundwater resilience zoning maps for extremely low, low, medium, relatively high, and high levels. The prediction accuracy of the final model was 51.8% (…). ), 66.7% ).by The results of the prediction verification are better than Based on eight groundwater control factors (topography, slope, distance to river, soil, canal density, permeability coefficient, groundwater depth, and land use) and Based on the spatial relationship between them, the groundwater resilience was divided into five categories: extremely low (Bel value 0-0.072), low (0.072-0.171), medium (0.171-0.458), relatively high (0.458-0.786), and high (>0.786), accounting for 3%, 30%, 30%, 22%, and 15% of the total area, respectively.

[0132] In summary, the prediction of regional groundwater resilience based on the DS model is reasonable and reliable. The model's advantage lies not only in its ability to output the degree of support for the prediction results through the belief function, but also in its ability to quantify the uncertainty of the prediction results, reflecting both systematic and random errors. Furthermore, the selection of groundwater control factors (converted into evidence layers in the DS model) significantly impacts the model's performance; a sufficiently detailed evidence layer may yield more accurate and reliable spatial predictions. This invention provides some assistance in understanding groundwater resilience in arid inland river basins, and the research findings may offer a scientific basis for groundwater resource planning and management by relevant departments in Hotan Prefecture.

[0133] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting the spatial distribution and uncertainty of groundwater resilience, characterized in that, The methods for predicting the spatial distribution and uncertainty of groundwater resilience include: Groundwater resilience is calculated using groundwater performance indicators and resilience index; the relationship between groundwater resilience and nine groundwater control factors is quantitatively analyzed based on GIS technology and the DS evidence conflict theory model, and a groundwater resilience zoning map is generated. The method for predicting the spatial distribution and uncertainty of groundwater resilience also includes: Based on two indicators, groundwater performance index and resilience index, DS models are constructed to predict the spatial resilience of groundwater. Nine groundwater control factors were selected and a spatial dataset was created as the evidence layer in the DS model. The fusion results of the DS model were used to spatially predict groundwater resilience and output a groundwater resilience zoning map. The method for predicting the spatial distribution and uncertainty of groundwater resilience includes the following steps: Step 1: Obtain monthly groundwater level data, calculate groundwater performance indicators and resilience index, and determine groundwater resilience. Step 2: Select 9 groundwater control factors as evidence layers to construct the DS model, and establish mathematical relationships between groundwater performance indicators and resilience index and groundwater control factors respectively. Step 3: Generate a spatial distribution map of groundwater resilience in a GIS environment based on evidence fusion; The calculation of groundwater performance indicators and resilience index, and the determination of groundwater resilience, include: First, calculate the groundwater performance indicators using the following formula: but ; otherwise ; ； in, Indicates groundwater performance indicators; This represents the multi-year average groundwater level. Represents the time series of groundwater levels in wells; satisfactory state Dissatisfied status This indicates that the groundwater level at the wellhead is lower than the multi-year average. Secondly, the restoring force of the groundwater system at time t is calculated using the following formula: ； =0.375 ； ； in, Indicates resilience index; Represents state variables exist Time and Proximity ( The threshold for the difference; and These represent the maximum and minimum values ​​of the monthly groundwater level data sequence, respectively. Step three, which involves generating a spatial distribution map of groundwater resilience in a GIS environment based on evidence fusion, includes: 1) Determine the basic probability allocation for each information layer based on the spatial location of the observation wells and the spatial relationship between the control factor layers of groundwater; 2) Overlay the attributes of the groundwater control factor layers to obtain raster layers with confidence, rejection, likelihood, and uncertainty for each groundwater control factor. 3) According to the evidence fusion rules, BPA fusion of different information layers is achieved by performing eight iterations on the evidence layer, and the evidence fusion result is output. The groundwater resilience zoning map is constructed using the evidence fusion result.

2. The method for predicting the spatial distribution and uncertainty of groundwater resilience as described in claim 1, characterized in that, The nine groundwater control factors include: lithology, topography, slope, distance to the river, soil, canal density, permeability coefficient, groundwater depth, and land use.

3. The method for predicting the spatial distribution and uncertainty of groundwater resilience as described in claim 1, characterized in that, In step two, nine groundwater control factors are selected as evidence layers to construct the DS model, and the mathematical relationships between groundwater performance indicators and resilience indices and groundwater control factors are established, including: A raster dataset of nine groundwater control factors was generated in a GIS environment, and the following formula was used based on groundwater performance indicators. and resilience index The calculated belief function components of the groundwater resilience control factors for each category clearly define the relationship between groundwater resilience and the nine control factors: ； ； ； ； in, Indicates the first The first layer of spatial information Class attributes, This indicates support for the hypothesis. The likelihood ratio, To show support for the hypothesis The likelihood ratio is calculated using the following formula: ； ； in, The middle supports the hypothesis The number of wells; This indicates that the hypothesis is supported across the entire region. The total number of wellheads; Indicates the first The first in the layer The total number of grid cells for class attributes; This represents the total number of grid cells in the entire area.

4. The method for predicting the spatial distribution and uncertainty of groundwater resilience as described in claim 1, characterized in that, The evidence fusion rules are as follows: ； ； ； ； ； in, Indicated by evidence The level of trust generated Indicated by evidence The resulting likelihood Indicated by evidence The resulting uncertainty is calculated as follows: , Indicated by evidence The resulting level of distrust is calculated as follows: ; This indicates the confidence level for each level of factor type or range. This indicates the level of distrust for each factor type or range. This indicates the uncertainty of the type or range of factors at each level. This indicates the 1st, 2nd, ..., 9th factor type. This is the normalization factor.

5. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for predicting the spatial distribution and uncertainty of groundwater resilience as described in any one of claims 1-4.

6. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for predicting the spatial distribution and uncertainty of groundwater resilience as described in any one of claims 1-4.

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