Method for verifying land utilization simulation space of CA model

Abstract

The embodiment of the invention discloses a method for verifying a land utilization simulation space of a CA model, and relates to the technical field of geographic simulation, and the method comprises the steps: obtaining the initial land utilization data and the last land utilization data of a research region during the simulation period of the CA model, and obtaining the simulated land utilization data outputted by the model; respectively converting the initial stage, the last stage and the simulated land utilization data into corresponding matrixes; and inputting the matrix into a Fuzzy FoM model, calculating to obtain evaluation parameters of the CA model, and outputting an evaluation result. According to the method, the evaluation parameters are calculated by comparing the cells with changes in the matrixes corresponding to the initial stage, the last stage and the simulated land utilization data, and the evaluation parameters are calculated by comparing the change conditions of the cells in the three kinds of land utilization data and combining the position fuzziness. Therefore, the accuracy of the CA model and the capturing capability of spatial autocorrelation and heterogeneity can be quantified, and the improvement direction of the CA model can be guided.

Description

Technical Field

[0001] The present application relates to the field of geographic simulation technology, and in particular to a method for verifying a land use simulation space of a CA model. Background Art

[0002] Cellular Automata (CA) is a "bottom-up" model that simulates spatially discrete and temporally discrete complex phenomena through local operations. Due to its open structure, it can be integrated with other models to simulate and predict land use evolution. CA has been widely used to simulate the spatiotemporal evolution of complex nonlinear systems and is very suitable for the simulation and prediction of complex geographical processes. The model's ability to capture spatial autocorrelation and heterogeneity has always been the focus of current research.

[0003] However, the current CA models have the following deficiencies in their ability to quantify spatial autocorrelation and capture heterogeneity:

[0004] (1) The traditional cell-by-cell inspection method ignores the model's ability to capture spatial autocorrelation and heterogeneity;

[0005] (2) The traditional Kappa coefficient for evaluating model accuracy has been questioned by the academic community. By comparing the examples of evaluating model accuracy in land use simulation and remote sensing neighborhoods, the Kappa coefficient is somewhat misleading and can cause the actual accuracy of the model to be less than the evaluation accuracy, especially when evaluating simulation results in areas with less land use changes.

[0006] Therefore, a new method is urgently needed to solve the above problems, quantify the ability of land use change simulation models to capture spatial autocorrelation and heterogeneity, and better evaluate the model's simulation results on land use change. Summary of the invention

[0007] The present application provides a method for verifying the land use simulation space of the CA model, which is used to solve the defects in the above-mentioned related technologies, and can quantitatively evaluate the CA model's ability to capture spatial autocorrelation and heterogeneity while correctly evaluating the accuracy of the model. The technical solution is as follows:

[0008] In a first aspect, an embodiment of the present application provides a method for verifying a land use simulation space of a CA model, characterized by comprising:

[0009] S1: obtaining the initial land use data and the final land use data of the study area during the simulation of the CA model, and obtaining the simulated land use data output by the CA model;

[0010] S2 converts the initial land use data, the final land use data and the simulated land use data into corresponding matrices respectively, where matrix elements correspond to cells and matrix space corresponds to cell space;

[0011] S3 inputs each matrix into the Fuzzy FoM model, calculates the evaluation parameters of the CA model through the Fuzzy FoM model, and outputs the evaluation result of the CA model based on the evaluation parameters.

[0012] In an optional solution of the first aspect, step S1 specifically includes the following steps:

[0013] S101 performs rasterization processing on the initial land use data, the final land use data and the simulated land use data respectively to obtain corresponding land use raster data;

[0014] S102 assigns the value of 0 to all the marginal non-study areas of the land use raster data.

[0015] In an optional solution of the first aspect, step S2 specifically includes the following steps:

[0016] Convert all land use raster data into land use state matrices respectively, determine the corresponding land use type according to the cell corresponding to each element in the land use state matrix, and assign values ​​to each element in the land use state matrix based on the land use type;

[0017] The land use types include cultivated land, forest land, grassland, water area, construction land, and unused land. The cultivated land is assigned a value of 1, the forest land is assigned a value of 2, the grassland is assigned a value of 3, the water area is assigned a value of 4, the construction land is assigned a value of 5, and the unused land is assigned a value of 6.

[0018] In an optional solution of the first aspect, step S3 specifically includes the following steps:

[0019] S301 extracts first cells based on all matrices through a first mask, takes each first cell as the center of a distance decay function, and calculates the degree of belonging of each cell; extracts second cells based on all matrices through a second mask, and extracts the degree of belonging of the second cells;

[0020] S302 calculates the sum of the degrees of belonging of all the second cells, and calculates an evaluation parameter based on the sum of the degrees of belonging;

[0021] Among them, the first cell is a cell in which the land use type changes during the simulation period and the CA model simulates incorrectly; the second cell is a cell in which the land use type in the initial land use data and the final land use data is not 1, but the land use type simulated in the simulated land use data is 1.

[0022] In an optional solution of the first aspect, in step S301, a first cell is extracted based on all matrices using a first mask, and the formula is applied:

[0023]

[0024] like If the calculated value is 1, the corresponding cell is extracted as the first cell;

[0025] Take each first cell as the center of the distance decay function, calculate the degree of belonging of each cell, and apply the formula:

[0026]

[0027] in, is the first mask for land use type l at position (i, j), is the land use type corresponding to the position (i, j) in the initial land use data, is the land use type corresponding to the position (i, j) in the final land use data, To simulate the land use type corresponding to the position (i, j) in the land use data, f(·) is the Gaussian distance decay function, and d is the Euclidean distance from the position (i, j) to the central cell.

[0028] In an optional solution of the first aspect, in step S301, a second cell is extracted based on all matrices using a second mask, and the formula is applied:

[0029]

[0030] like The calculated value is 1, and the corresponding cell is extracted as the second cell;

[0031] retaining the second cell through the second mask, and obtaining the degree of belonging corresponding to the second cell;

[0032] The calculation obtains the sum of the degrees of belonging of all the second cells, and the formula is applied:

[0033]

[0034] Where E represents the sum of the degrees of belonging of all cells of spatial autocorrelation and heterogeneity in the study area, num represents the number of land use types, Indicates the maximum value of the degree of belonging of the cell with land use type l at the position (i, j) among all Gaussian distance decay functions, Represents the second mask for land use type l at position (i, j).

[0035] In an optional solution of the first aspect, in step S302, the evaluation parameter is calculated based on the sum of the attribution degrees, and the formula is applied:

[0036]

[0037] Outputting the evaluation result of the CA model based on the evaluation parameters in step S3 specifically includes the following steps:

[0038] Visualize the Fuzzy FoM and output a distribution map of the accuracy of the CA model in simulating the land use types in the study area;

[0039] Among them, FoM fuzzy is the value of the evaluation parameter, A is the area where the actual land use type has changed but the simulation result has not been correctly simulated; B is the area where both the actual land use and the simulation result have changed and the simulation has been correct; C is the area where both the actual land use and the simulation result have changed but the simulation has been wrong; D is the area where the actual land use type has not changed but the simulation result has not changed.

[0040] In a second aspect, an embodiment of the present application further provides a device for verifying a land use simulation space of a CA model, comprising:

[0041] A data acquisition module, used to acquire the initial land use data and the final land use data of the study area during the simulation of the CA model, and to acquire the simulated land use data output by the CA model;

[0042] A data processing module, used for converting the initial land use data, the final land use data and the simulated land use data into corresponding matrices, wherein matrix elements correspond to cells and matrix space corresponds to cell space;

[0043] The evaluation module is used to input each matrix into the Fuzzy FoM model, calculate the evaluation parameters of the CA model through the Fuzzy FoM model, and output the evaluation result of the CA model based on the evaluation parameters.

[0044] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect is implemented.

[0045] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiment of the present application or any one of the implementations of the first aspect.

[0046] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0047] (1) The Fuzzy FoM model provided in this application introduces location fuzziness when simulating land use data, that is, a certain degree of attribution is given within a specific surrounding range, thereby capturing the spatial autocorrelation and heterogeneity characteristics, and quantifying the degree of attribution by establishing a Gaussian distance decay function, which is very suitable for land use simulation research.

[0048] (2) The Fuzzy FoM model provided in this application calculates the evaluation parameters by comparing the cells with changes in the matrix corresponding to the initial, final and simulated land use data. By comparing the changes in the cells in these three land use data and combining the location fuzziness, the accuracy of the CA model and the ability of the CA model to capture spatial autocorrelation and heterogeneity can be quantified, which helps to guide the improvement direction of the CA model. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a flowchart of a method for verifying a land use simulation space of a CA model provided in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of processing using a mask in an embodiment of the present application;

[0052] Figure 3 is a schematic diagram of the evaluation results of Fuzzy FoM taking Beijing as an example in the embodiments of the present application;

[0053] Figure 4is a schematic diagram of the evaluation results of Fuzzy FoM taking Wuhan as an example in the embodiments of the present application;

[0054] Figure 5 It is a structural schematic diagram of a device for verifying a land use simulation space of a CA model provided in an embodiment of the present application;

[0055] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0058] It should be noted that the terms "first\second" involved in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those described or illustrated herein.

[0059] The present application is described in detail below with reference to specific embodiments.

[0060] Next, combine Figure 1 , introduces a method for verifying the land use simulation space of the CA model provided by the embodiment of the present application. For details, please refer to Figure 1 , Figure 1 A schematic diagram of a method for verifying a land use simulation space of a CA model provided in an embodiment of the present application is shown. Figure 1 As shown, the method comprises the following steps:

[0061] S1: obtaining the initial land use data and the final land use data of the study area during the simulation of the CA model, and obtaining the simulated land use data output by the CA model;

[0062] S2 converts the initial land use data, the final land use data and the simulated land use data into corresponding matrices respectively, where matrix elements correspond to cells and matrix space corresponds to cell space;

[0063] S3 inputs each matrix into the Fuzzy FoM model, calculates the evaluation parameters of the CA model through the Fuzzy FoM model, and outputs the evaluation result of the CA model based on the evaluation parameters.

[0064] In some embodiments, step S1 specifically includes the following steps:

[0065] S101 performs rasterization processing on the initial land use data, the final land use data and the simulated land use data respectively to obtain corresponding land use raster data;

[0066] S102 assigns the value of 0 to all the marginal non-study areas of the land use raster data.

[0067] Specifically, the initial land use data can be understood as the actual land use data in the initial stage of simulating the study area using the CA model, the final land use data can be understood as the actual land use data in the final stage of simulating the study area using the CA model, and the simulated land use data can be understood as the simulation result data of the CA model simulating the study area.

[0068] Specifically, in S101 , the initial, final, and simulated land use grid data may be aligned according to the actual geographic coordinates of each grid.

[0069] In some embodiments, step S2 specifically includes the following steps:

[0070] Convert all land use raster data into land use state matrices respectively, determine the corresponding land use type according to the cell corresponding to each element in the land use state matrix, and assign values ​​to each element in the land use state matrix based on the land use type;

[0071] The land use types include cultivated land, forest land, grassland, water area, construction land, and unused land. The cultivated land is assigned a value of 1, the forest land is assigned a value of 2, the grassland is assigned a value of 3, the water area is assigned a value of 4, the construction land is assigned a value of 5, and the unused land is assigned a value of 6.

[0072] It can be understood that each grid can be regarded as a cell, and the coordinates of the center point of the cell can be selected as the coordinates of the corresponding cell. Each element in the matrix corresponds to the position coordinates of the cell according to the row and column in which it is located.

[0073] In some embodiments, step S3 specifically includes the following steps:

[0074] S301 extracts first cells based on all matrices through a first mask, takes each first cell as the center of a distance decay function, and calculates the degree of belonging of each cell; extracts second cells based on all matrices through a second mask, and extracts the degree of belonging of the second cells;

[0075] S302 calculates the sum of the degrees of belonging of all the second cells, and calculates an evaluation parameter based on the sum of the degrees of belonging;

[0076] Among them, the first cell is a cell in which the land use type changes during the simulation period and the CA model simulates incorrectly; the second cell is a cell in which the land use type in the initial land use data and the final land use data is not 1, but the land use type simulated in the simulated land use data is 1.

[0077] Specifically, in step S301, the first cell is extracted based on all matrices through the first mask, and the formula is applied:

[0078]

[0079] like If the calculated value is 1, the corresponding cell is extracted as the first cell;

[0080] Take each first cell as the center of the distance decay function, calculate the degree of belonging of each cell, and apply the formula:

[0081]

[0082] in, is the first mask for land use type l at position (i, j), is the land use type corresponding to the position (i, j) in the initial land use data, is the land use type corresponding to the position (i, j) in the final land use data, To simulate the land use type corresponding to the position (i, j) in the land use data, f(·) is the Gaussian distance decay function, and d is the Euclidean distance from the position (i, j) to the central cell.

[0083] It should be noted that the degree of attribution can be understood as the correct trend of the simulation of the surrounding cells relative to the first cell. The higher the value of the degree of attribution, the higher the probability that the CA model correctly simulates the land use type of each cell. It can be understood that when calculating the distance based on the Gaussian distance decay function, only the degree of attribution of the cells within the decay radius is calculated.

[0084] In step S301, the second cell is extracted based on all matrices through the second mask, and the formula is applied:

[0085]

[0086] like The calculated value is 1, and the corresponding cell is extracted as the second cell;

[0087] retaining the second cell through the second mask, and obtaining the degree of belonging corresponding to the second cell;

[0088] The calculation obtains the sum of the degrees of belonging of all the second cells, and the formula is applied:

[0089]

[0090] Where E represents the sum of the degrees of belonging of all cells of spatial autocorrelation and heterogeneity in the study area, num represents the number of land use types, Indicates the maximum value of the degree of belonging of the cell with land use type l at the position (i, j) among all Gaussian distance decay functions, Represents the second mask for land use type l at position (i, j).

[0091] For example, Figure 2 , which is a schematic diagram of extracting cells for calculating the degree of attribution using the first mask Mask I and Mask II. Figure 2 In the figure, it can be found that there are 3 cells whose land use type is forest land in the initial land use map and construction land in the final land use map, but the land use type in the final simulation result is forest land. Therefore, 3 cells can be extracted through the first mask; then the degree of belonging of other cells around these three cells can be calculated. Specifically, the Gaussian distance decay function in the above embodiment can be used to calculate the Euclidean distance from the central cell at each cell position, and then the value of the degree of belonging of each cell within the decay radius can be calculated, and it can be divided into three categories according to the value of the degree of belonging, namely, high degree of belonging, low degree of belonging and no degree of belonging. Subsequently, the second cell is derived through the second mask, that is, the cell that is forest land in both the initial and final land use maps but simulated as construction land by the CA model, and the degree of belonging of the selected second cell is extracted.

[0092] Finally, the correct simulated cells and the cells with belonging degree and within the attenuation radius are superimposed to generate Figure 2 The visualization map of Fuzzy FoM shown in the figure; by traversing each grid of each land use type, the visualization map obtained can obtain the final evaluation result distribution map of the CA model simulating the study area.

[0093] In some embodiments, in step S302, the evaluation parameter is calculated based on the sum of the degrees of belonging, and the formula is applied:

[0094]

[0095] Outputting the evaluation result of the CA model based on the evaluation parameters in step S3 specifically includes the following steps:

[0096] Visualize the Fuzzy FoM and output a distribution map of the accuracy of the CA model in simulating the land use types in the study area;

[0097] Among them, FoM fuzzy is the value of the evaluation parameter, A is the area where the actual land use type has changed but the simulation result has not been correctly simulated; B is the area where both the actual land use and the simulation result have changed and the simulation has been correct; C is the area where both the actual land use and the simulation result have changed but the simulation has been wrong; D is the area where the actual land use type has not changed but the simulation result has not changed.

[0098] In some embodiments, the land use data and simulation results of Beijing and Wuhan from 2000 to 2010 can be used as examples to analyze the accuracy evaluation results of Fuzzy FoM. Figure 3-4 As shown, Figure 3 This is a schematic diagram of the evaluation results of Fuzzy FoM taking Beijing as an example. Figure 4 The following is a schematic diagram of the evaluation results of Fuzzy FoM, taking Wuhan as an example. In order to obtain the training data required for the experiment, ArcGIS tools can be used to perform spatial overlay analysis on land use data, extract urban land use change and spatial variable data, and perform data standardization and spatial projection.

[0099] Specifically, the construction and operation of an evaluation index for spatial autocorrelation and heterogeneity of land use simulation to verify the CA model were implemented in the Matlab2022b environment. The computer configuration is Intel i7-10 generation, 32G memory, and 6G 6500XT graphics card. In order to demonstrate the ability of Fuzzy FoM to capture spatial autocorrelation and heterogeneity, artificial neural network (ANN) CA, PLUS model, ASHN (G)-CA and ASHN (E)-CA were compared.

[0100] Table 1 Evaluation of land use change simulation accuracy in Beijing and Wuhan

[0101]

[0102] Table 1 shows that in Beijing, the FoM value of the ANN-CA model is 0.2564, which exceeds the 0.1071 of the PLUS model, while the Fuzzy FoM value of the PLUS model is 0.1699 higher than its FoM value, significantly exceeding the 0.0673 of the ANN-CA model. This shows that the ANN-CA model has a greater advantage in modeling accuracy, while the PLUS model has a stronger ability to capture spatial features. In addition, a comparison of the accuracy of the models in Wuhan shows that the performance of the ANN-CA model drops sharply compared with that in Beijing, while the accuracy gap of the PLUS model is smaller. In addition, the enhancement of the Fuzzy FoM of the PLUS model is more obvious in both study areas, indicating that the PLUS model is more stable and proficient in capturing spatial autocorrelation and heterogeneity in land use change simulation. The gap between the two models is more obvious. In both study areas, the FoM value of the ASHN (E) model is slightly lower than that of the ASHN (G). However, the fuzzy FoM value of the ASHN(E) model is improved from 0.085 to 0.1006, which is higher than that of the ASHN(G) model from 0.044 to 0.0771. Therefore, the ASHN-CA(E) model has advantages in both model accuracy and spatial feature capture.

[0103] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0104] See next Figure 5, which is a schematic diagram of the structure of a device for verifying the land use simulation space of the CA model provided by an exemplary embodiment of the present application. The device can be implemented as all or part of the terminal through software, hardware, or a combination of both, and can also be integrated on the server as an independent module. The device for verifying the land use simulation space of the CA model in the embodiment of the present application can be applied to the terminal or the cloud. The device 50 includes a data acquisition module 501, a data processing module 502, and an evaluation module 503, wherein:

[0105] The data acquisition module 501 is used to acquire the initial land use data and the final land use data of the study area during the simulation of the CA model, and to acquire the simulated land use data output by the CA model;

[0106] The data processing module 502 is used to convert the initial land use data, the final land use data and the simulated land use data into corresponding matrices, where matrix elements correspond to cells and matrix space corresponds to cell space;

[0107] The evaluation module 503 is used to input each matrix into the Fuzzy FoM model, calculate the evaluation parameters of the CA model through the Fuzzy FoM model, and output the evaluation result of the CA model based on the evaluation parameters.

[0108] It should be noted that, when the device 50 provided in the above embodiment executes the method for verifying the land use simulation space of the CA model, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the method embodiment for verifying the land use simulation space of the CA model belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0109] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the program.

[0110] See also Figure 6 , which is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0111] like Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 .

[0112] In the embodiment of the present application, the processor 601 is the control center of the computer system, which can be a processor of a physical machine or a processor of a virtual machine. The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0113] The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also called a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0114] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one instruction, which is used to be executed by the processor 601 to implement the method in the embodiment of the present application.

[0115] In some embodiments, the electronic device 600 further includes: a peripheral device interface 603 and at least one peripheral device 604. The processor 601, the memory 602 and the peripheral device interface 603 can be connected via a bus or a signal line. Each peripheral device 604 can be connected to the peripheral device interface 603 via a bus, a signal line or a circuit board. Specifically, the peripheral device 604 includes: a display screen, a camera and an audio circuit. The peripheral device interface 603 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 601 and the memory 602.

[0116] In some embodiments of the present application, the processor 601, the memory 602, and the peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 601, the memory 602, and the peripheral device interface 603 can be implemented on a separate chip or circuit board. This embodiment of the present application does not specifically limit this.

[0117] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 600. The electronic device 600 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0118] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for verifying the land use simulation space of a CA model, characterized in that: include: S1: obtaining the initial land use data and the final land use data of the study area during the simulation of the CA model, and obtaining the simulated land use data output by the CA model; S2 converts the initial land use data, the final land use data and the simulated land use data into corresponding matrices respectively, where matrix elements correspond to cells and matrix space corresponds to cell space; S3 inputs each matrix into the Fuzzy FoM model, calculates the evaluation parameters of the CA model through the Fuzzy FoM model, and outputs the evaluation result of the CA model based on the evaluation parameters.

2. A method for verifying the land use simulation space of the CA model according to claim 1, characterized in that: Step S1 specifically includes the following steps: S101 performs rasterization processing on the initial land use data, the final land use data and the simulated land use data respectively to obtain corresponding land use raster data; S102 assigns the value of 0 to all the marginal non-study areas of the land use raster data.

3. A method for verifying the land use simulation space of the CA model according to claim 2, characterized in that: Step S2 specifically includes the following steps: Convert all land use raster data into land use state matrices respectively, determine the corresponding land use type according to the cell corresponding to each element in the land use state matrix, and assign values ​​to each element in the land use state matrix based on the land use type; The land use types include cultivated land, forest land, grassland, water area, construction land, and unused land. The cultivated land is assigned a value of 1, the forest land is assigned a value of 2, the grassland is assigned a value of 3, the water area is assigned a value of 4, the construction land is assigned a value of 5, and the unused land is assigned a value of 6.

4. The method for verifying the land use simulation space of the CA model according to claim 1, characterized in that: Step S3 specifically includes the following steps: S301 extracts first cells based on all matrices through a first mask, takes each first cell as the center of a distance decay function, and calculates the degree of belonging of each cell; extracts second cells based on all matrices through a second mask, and extracts the degree of belonging of the second cells; S302 calculates the sum of the degrees of belonging of all the second cells, and calculates an evaluation parameter based on the sum of the degrees of belonging; Among them, the first cell is a cell in which the land use type changes during the simulation period and the CA model simulates incorrectly; the second cell is a cell in which the land use type in the initial land use data and the final land use data is not 1, but the land use type simulated in the simulated land use data is 1.

5. A method for verifying land use simulation space of CA model according to claim 4, characterized in that: In step S301, the first cell is extracted based on all matrices through the first mask, and the formula is applied: like If the calculated value is 1, the corresponding cell is extracted as the first cell; Take each first cell as the center of the distance decay function, calculate the degree of belonging of each cell, and apply the formula: in, is the first mask for land use type l at position (i, j), is the land use type corresponding to the position (i, j) in the initial land use data, is the land use type corresponding to the position (i, j) in the final land use data, To simulate the land use type corresponding to the position (i, j) in the land use data, f(·) is the Gaussian distance decay function, and d is the Euclidean distance from the position (i, j) to the central cell.

6. A method for verifying the land use simulation space of the CA model according to claim 5, characterized in that: In step S301, the second cell is extracted based on all matrices through the second mask, and the formula is applied: like The calculated value is 1, and the corresponding cell is extracted as the second cell; retaining the second cell through the second mask, and obtaining the degree of belonging corresponding to the second cell; The calculation obtains the sum of the degrees of belonging of all the second cells, and the formula is applied: Where E represents the sum of the degrees of belonging of all cells of spatial autocorrelation and heterogeneity in the study area, num represents the number of land use types, Indicates the maximum value of the degree of belonging of the cell with land use type l at the position (i, j) among all Gaussian distance decay functions, Represents the second mask for land use type l at position (i, j).

7. A method for verifying land use simulation space of a CA model according to claim 6, characterized in that: In step S302, the evaluation parameter is calculated based on the sum of the attribution degrees, and the formula is applied: Outputting the evaluation result of the CA model based on the evaluation parameters in step S3 specifically includes the following steps: Visualize the Fuzzy FoM and output a distribution map of the accuracy of the CA model in simulating the land use types in the study area; Among them, FoM fuzzy is the value of the evaluation parameter, A is the area where the actual land use type has changed but the simulation result has not been correctly simulated; B is the area where both the actual land use and the simulation result have changed and the simulation has been correct; C is the area where both the actual land use and the simulation result have changed but the simulation has been wrong; D is the area where the actual land use type has not changed but the simulation result has not changed.

8. A device for verifying the land use simulation space of the CA model, characterized in that: include: A data acquisition module, used to acquire the initial land use data and the final land use data of the study area during the simulation of the CA model, and to acquire the simulated land use data output by the CA model; A data processing module, used for converting the initial land use data, the final land use data and the simulated land use data into corresponding matrices, wherein matrix elements correspond to cells and matrix space corresponds to cell space; The evaluation module is used to input each matrix into the Fuzzy FoM model, calculate the evaluation parameters of the CA model through the Fuzzy FoM model, and output the evaluation result of the CA model based on the evaluation parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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