River channel hydrodynamic model construction method based on multi-source remote sensing

Abstract

The invention relates to the technical field of model construction, and provides a river channel hydrodynamic model construction method based on multi-source remote sensing, and the method comprises the steps: obtaining a river channel remote sensing image so as to obtain a waterline, obtaining water level data through a height measurement satellite, and giving an elevation value to a point location corresponding to the waterline according to the waterline and the water level data; the straight river reach is valued according to a specific rule, and the curved river reach is determined according to linear fitting of a plurality of elevation measuring point values on the river section; based on the waterline elevation values of the straight and curved river reach, interpolation is further carried out along the waterline, a riverway terrain digital grid model is constructed, a riverway hydrodynamic model is constructed by using the riverway terrain digital grid model, and the simulation effect of the hydrodynamic model is judged by comparing the water level consistency or the water surface vector form consistency. And calibration and verification of the river hydrodynamic model are realized. And accurate simulation of the river hydrodynamic process is realized. According to the method, the model parameter acquisition cost can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of model construction, and in particular relates to a method for constructing a river hydrodynamic model based on multi-source remote sensing. Background Art

[0002] The river hydrodynamic model is the basis for simulating the river hydrodynamic process and the associated pollutant transport, and is an important tool for river management and decision-making support. The construction of a classic river hydrodynamic model requires first conducting river topography surveys, then performing river calculation grid division based on the topography, and then setting the model boundary conditions, using the existing river water level and flow data to calibrate and verify the model parameters.

[0003] The measurement of river topography includes ground measurement, aerial photogrammetry and underwater topography measurement. Ground measurement uses equipment such as total stations and levels to obtain high-precision topographic data by manually laying out control points and measuring points. Although it has high accuracy, it is cumbersome to operate and difficult to implement in complex terrain. Aerial photogrammetry uses drones or aircraft equipped with cameras to generate digital elevation models (DEMs) through image processing. It has high spatial resolution and fast operation capabilities, but is affected by weather conditions and has high costs. Underwater topography measurement mainly relies on the large-section method of hydrological stations and unmanned boats equipped with acoustic Doppler current profilers (ADCPs). Hydrological stations regularly measure water depth and flow velocity through fixed sections to obtain underwater topographic data. It is suitable for fixed sites, but the coverage is limited and the data update frequency is low. Unmanned boats equipped with ADCPs can measure flow velocity and water depth at different locations and depths to generate detailed underwater topographic maps. It has high flexibility and accuracy, but the equipment cost is high and is limited by the water environment.

[0004] The roughness coefficient of the computational grid is assigned according to the characteristics of the river section. This method does not take into account the spatial heterogeneity of the river and cannot achieve distributed assignment based on the division of the hydrodynamic model calculation unit. For example, different sections of the same river may have different underlying surface types and water flow characteristics. Using a unified roughness coefficient for assignment will lead to inaccurate simulation results.

[0005] Model parameter calibration requires actual water level and flow monitoring data. In traditional methods, monitoring data mostly comes from hydrological stations set up at fixed sections, which regularly record flow and water level through manual or automated equipment. However, this fixed-site monitoring method has the problems of limited data coverage and low update frequency, especially in areas with complex river morphology or frequent human activities, it is difficult to obtain comprehensive hydrological information. In addition, the establishment and long-term operation of hydrological stations often require high infrastructure and maintenance costs. Summary of the invention

[0006] To solve the above problems, the present invention provides a method for constructing a river hydrodynamic model based on multi-source remote sensing, which adopts remote sensing images and altimetry satellite data, and solves the problems of limited coverage, low update frequency and high cost of traditional underwater topographic measurement by obtaining water edges, assigning elevation values ​​to different river points, and constructing terrain and hydrodynamic models.

[0007] The following is the technology of the present invention:

[0008] A method for constructing a river hydrodynamic model, characterized by comprising:

[0009] Obtain remote sensing images of the river, obtain river water body vector files based on the remote sensing images, and obtain the water edge line;

[0010] The water level data at the corresponding water edge of the river channel is obtained by using an altimetry satellite; based on the obtained water edge and the corresponding water level data, the elevation values ​​are respectively assigned to the corresponding points of the water edge of the straight river section and the curved river section;

[0011] For straight river sections, when the point measured by the altimeter satellite coincides with the river section, the elevation value at the intersection of the altimeter satellite point and the waterside line is taken as the elevation value of the waterside line; if there is an angle between the altimeter satellite and the flow section, the elevation value at the intersection of the altimeter satellite point and the flow section is selected as the elevation value of the point on the waterside line corresponding to the flow section; for curved river sections, linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside line corresponding to the curved river section is obtained based on the fitting relationship;

[0012] Based on the waterside elevation values ​​of straight river sections and curved river sections, a digital grid model of river terrain is constructed; the river hydrodynamic model is constructed using the digital grid model of river terrain;

[0013] The simulation effect of the hydrodynamic model is determined by comparing the consistency of water levels or water surface vector shapes, thereby achieving calibration and verification of the river hydrodynamic model.

[0014] Further,

[0015] The method of constructing a river hydrodynamic model using a digital grid model of river terrain comprises:

[0016] Based on the reconstructed river terrain, the hydrodynamic model is calculated and gridded to obtain a gridded river terrain model;

[0017] Calculate the grid roughness of the gridded river terrain model;

[0018] Determine the boundary conditions of the river hydrodynamic model;

[0019] A river hydrodynamic model is constructed based on grid roughness, boundary conditions and gridded river terrain model.

[0020] Further,

[0021] For the curved river section, linear fitting is performed according to the values ​​of multiple elevation measuring points on the river section, and the elevation value at the waterside corresponding to the curved river section is obtained based on the fitting relationship; based on the waterside elevation values ​​of the straight river section and the curved river section, a digital grid model of the river terrain is constructed; including:

[0022] Linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section. After the elevation value of the waterside corresponding to the curved river section is obtained based on the fitting relationship, the missing waterside is interpolated to obtain complete elevation data:

[0023] Get the water edge line of the highest water level as the outer envelope line;

[0024] Taking the elevation value corresponding to the outer envelope of the water edge as the maximum interpolation range, the elevation value on the water edge is interpolated by the interpolation method to obtain the complete elevation data;

[0025] Points with equal elevation values ​​are connected to form contour lines, and digital grid models of river terrain are further generated based on these contour lines.

[0026] Further,

[0027] The calculation of mesh roughness comprises:

[0028] Based on remote sensing images, the roughness ratios in dry season and non-dry season are calculated respectively;

[0029] The determination of the dry season roughness rate includes:

[0030] The color remote sensing image is converted into a grayscale image using the gray-level co-occurrence matrix texture feature extraction method.

[0031] Determine the grayscale level, select a distance d and direction θ, and for each pixel in the grayscale image, count the frequency of occurrence of the grayscale value pair at the distance d and direction θ to form a co-occurrence matrix P(i, j, d, θ), and extract multiple texture features from the co-occurrence matrix;

[0032] The roughness value is determined based on various texture characteristics.

[0033] Further,

[0034] Determining the boundary conditions of the river hydrodynamic model includes:

[0035] Use remote sensing images to obtain the water edge at the start of the model; assign elevation values ​​to the water edge, generate a water surface elevation file, and obtain the initial water level;

[0036] For the upstream, flow rate is used as the boundary condition;

[0037] For the downstream, the water level data recorded by the downstream section hydrological station are used as boundary conditions.

[0038] Further,

[0039] The various texture features include:

[0040] The uniformity of grayscale distribution, the contrast of grayscale, the degree of disorder of grayscale distribution, the homogeneity of pixel pairs in the image, and the linear relationship between pixel grayscales.

[0041] Further,

[0042] The method of determining the simulation effect of the hydrodynamic model by comparing the consistency of water levels or the consistency of water surface vector forms, and realizing the calibration and verification of the river hydrodynamic model, includes:

[0043] The measured data is used to perform calculation comparison with the data of the hydrodynamic model to achieve parameter calibration and verification of the hydrodynamic model;

[0044] The measured data and the data of the hydrodynamic model are used for calculation and comparison, and the calculated values ​​used for comparison include: Jaccard coefficient, Hausdorff distance, shape similarity, intersection area percentage, and Fréchet distance.

[0045] A system for constructing a river hydrodynamic model, characterized by comprising:

[0046] The waterside acquisition module is used to obtain the remote sensing image of the river, obtain the river water body vector file based on the remote sensing image, and obtain the waterside line;

[0047] The elevation value assignment module is used to obtain the water level data at the water edge of the river channel using the altimetry satellite; according to the acquired water edge and the corresponding water level data, the elevation values ​​are assigned to the corresponding points of the water edge of the straight river section and the curved river section respectively;

[0048] For straight river sections, when the point measured by the altimeter satellite coincides with the river section, the elevation value at the intersection of the altimeter satellite point and the waterside line is taken as the elevation value of the waterside line; if there is an angle between the altimeter satellite and the flow section, the elevation value at the intersection of the altimeter satellite point and the flow section is selected as the elevation value of the point on the waterside line corresponding to the flow section; for curved river sections, linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside line corresponding to the curved river section is obtained based on the fitting relationship;

[0049] The model building module is used to build a digital grid model of the river terrain based on the water edge elevation values ​​of the straight river section and the curved river section; and to build a river hydrodynamic model using the digital grid model of the river terrain;

[0050] The model verification module is used to determine the simulation effect of the hydrodynamic model by comparing the consistency of water level or water surface vector morphology, so as to realize the calibration and verification of the river hydrodynamic model.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] The present invention obtains remote sensing images of river channels and water level data from altimetry satellites, uses remote sensing images to observe the river surface in a larger spatial range to expand the coverage, and updates data more flexibly based on satellite revisit cycles, thereby making up for the limited coverage and low update frequency of hydrological stations. At the same time, it avoids the high cost and environmental restrictions caused by the need to deploy equipment in waters when unmanned ships are equipped with ADCPs.

[0053] In terrain construction, for straight rivers, the elevation value is determined based on the intersection of the altimetry satellite measuring point and the waterside or flow section, and the measurement data is directly used to ensure accuracy. Regardless of the relationship between the measuring point and the section, reliable elevation can be obtained to provide accurate basic data for the construction of the digital grid model; while the curved river has a lateral gradient due to centrifugal force, and the elevation at the waterside is determined by linear fitting of multiple elevation measuring point values. This method is based on the law of terrain changes in the curved river, can capture the gradual trend of elevation, and make the model fit the reality. Specific methods are used to assign elevation values ​​for straight and curved rivers, respectively, and the morphological characteristics of the river are fully considered. The constructed digital grid model can more accurately reflect the underwater terrain, which is more comprehensive and accurate than the large-section method of the hydrological station and the unmanned ship measurement.

[0054] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A schematic diagram of the method of the present invention is shown. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0058] Figure 1 A schematic diagram of the method according to the present invention is shown, and the specific implementation details of the present invention include:

[0059] 1. Step 1: Construct river terrain:

[0060] Specifically, they include:

[0061] (1-1) Based on high-resolution remote sensing images, obtain river water body vector files of 20 to 30 hydrological years, and fuse them to solve the maximum area water edge (the boundary between the water body and the land boundary when the water level reaches the highest) and the minimum area water edge (the boundary between the water body and the land boundary when the water level reaches the lowest);

[0062] Specific:

[0063] In order to improve the accuracy of terrain construction, ICESat-2 satellite data is used for auxiliary measurement. The ICESat-2 observatory is equipped with a light counting lidar (ATLAS instrument) as well as GPS, cameras and ground processing systems. These devices accurately determine the geodetic longitude and altitude of the reflection point by measuring the time it takes for the laser pulse to be emitted from ATLAS to reflect from the surface and back to the satellite. The ATLAS laser produces three pairs of left / right light spots on the surface, each pair of light spots depicting a ground track about 14 meters wide, forming six ground tracks that cover a cross-track distance of about 3 kilometers.

[0064] In these ground tracks, the left and right spots in each pair are about 90 meters apart in the cross-track direction and about 2.5 kilometers apart in the along-track direction. Higher-level ICESat-2 data products (such as ATL03 and above) are organized by ground track number and form pairs based on left and right spots, such as ground tracks 1L and 1R, 2L and 2R, and 3L and 3R.

[0065] Through this precise measurement, ICESat-2 can provide high-precision data support for the waterside elevation measurement of river sections, especially in bends with complex terrain. This data greatly improves the accuracy and reliability of terrain construction based on water surface lines.

[0066] (1-2) Based on the water edge data and hydrological station data at different times, the water level data of the altimetry satellite at the corresponding time is obtained;

[0067] (1-3) Assign elevation values ​​to corresponding points on the waterside line in straight river sections and curved river sections, and use interpolation method to assign elevation values ​​to the waterside line;

[0068] Straight river section:

[0069] If the point measured by the altimeter satellite coincides with the river section, take the elevation value at the intersection of the altimeter satellite point and the flow section.

[0070] If there is an angle between the altimeter satellite and the flow section, select the elevation value at the intersection of the altimeter satellite point and the flow section. The elevation point at the intersection represents the elevation point on the flow section. Directly assign the elevation value at the intersection to the point on the corresponding water edge of the corresponding flow section, thereby obtaining the elevation values ​​on both sides of the section.

[0071] Bend of the river:

[0072] In curved river sections, there will be a lateral gradient due to the existence of centrifugal force, so the elevation values ​​on the cross section will be different. It is necessary to perform linear fitting based on the values ​​of several existing elevation measuring points, and determine the elevation value at the water's edge corresponding to the river section based on the fitting relationship.

[0073] The n extracted waterside lines are merged to generate the outer envelope line. The outer envelope line represents the waterside line with the highest water level. With the outer envelope line as the maximum interpolation range, the elevation values ​​on each waterside line are interpolated to generate complete elevation data.

[0074] (1-4) Generate river terrain file.

[0075] The interpolated elevation data are used in the geographic information system tool to generate a digital raster model of the river terrain using the "create raster from contour lines" method.

[0076] 2. Step 2: Grid division for river channel calculation

[0077] The river terrain reconstructed in step 1 is used to perform grid division for hydrodynamic model calculation. Since the wall is affected by viscosity, the wall is encrypted and a structured grid is used. The radial boundaries are encrypted, and the water flow structure at the bend is complex, so the grid of the bend section needs to be encrypted, and finally the river calculation grid is obtained.

[0078] 3. Step 3: Calculate the grid roughness distribution assignment

[0079] Specifically, they include:

[0080] (3-1) Based on high-resolution remote sensing images, four seasonal images are selected to assign distributed roughness values ​​to the calculation units from the perspectives of dry season and non-dry season.

[0081] (3-2) Steps for calculating the roughness of the unit in the dry season:

[0082] In the dry season, water flows in the river channel, and the landforms within the water surface are relatively simple. The roughness coefficient is determined by analyzing the texture characteristics of the water surface. If the water surface has obvious texture characteristics, it indicates that there are more gravels, pebbles or large boulders on the riverbed, and the roughness coefficient of this type of river section is higher. If the water surface has no obvious texture characteristics, it indicates that the riverbed is relatively smooth, usually bedrock or stone riverbed, and the roughness coefficient of this type of river section is lower. Table 1 shows the corresponding roughness values ​​of the riverbed material:

[0083] Table 1 Corresponding riverbed material roughness value table

[0084]

[0085] Step 1: Remote sensing image texture feature extraction

[0086] The gray level co-occurrence matrix (GLCM) texture feature extraction method is used to convert color remote sensing images into grayscale images to reduce the amount of calculation and determine the appropriate grayscale level (for example, 256 levels of grayscale). A distance d and direction θ (0°, 45°, 90°, 135°) are selected, and for each pixel, the frequency of occurrence of grayscale value pairs at distance d and direction θ is counted to form a co-occurrence matrix P (i, j, d, θ), and a variety of texture features are extracted from the co-occurrence matrix.

[0087] The roughness value is determined based on 5 characteristic parameters:

[0088] a. Energy E: The uniformity of grayscale distribution. The greater the energy, the more regular and uniform the texture pattern. Value range: 0~1.

[0089] The higher the energy, the more uniform the image grayscale, the smoother the texture, and the lower the roughness. The lower the energy, the more uneven the grayscale distribution, the more complex the texture, and the greater the roughness.

[0090]

[0091] b. Contrast: Indicates the strength of the contrast between texture primitives. The higher the contrast, the greater the image roughness. The value range is 0 to infinity.

[0092] The higher the contrast, the more obvious the grayscale difference, the rougher the texture, and the greater the roughness. The lower the contrast, the less obvious the grayscale change, the smoother the image, and the lower the roughness.

[0093]

[0094] c. Entropy: It indicates the uncertainty and disorder of grayscale distribution and is an indicator to measure the complexity of an image. Its value range is greater than 0 and has no fixed upper limit.

[0095] The larger the entropy, the more complex the image, the messier the texture, and the greater the roughness; the smaller the entropy, the more ordered the image, the simpler the texture, and the lower the roughness.

[0096]

[0097] d. Homogeneity: Homogeneity measures the similarity of pixel pairs in an image. A higher value indicates a smoother texture. The value range is 0 to 1.

[0098] The higher the homogeneity, the smaller the pixel difference, the smoother the image, and the lower the roughness; the lower the homogeneity, the larger the pixel difference, the more complex the texture, and the greater the roughness.

[0099]

[0100] e. Correlation: Correlation reflects the linear relationship between pixels in an image. The value range is -1 to 1.

[0101] The higher the correlation, the stronger the linear relationship between the grayscale pixels in the image, the more ordered the texture, and the lower the roughness; the lower the correlation, the weaker the relationship between the pixels, the more disordered the image, and the greater the roughness.

[0102]

[0103] in, is the mean gray level in the x direction.

[0104] is the mean gray level in the y direction.

[0105] is the variance of the gray level in the x direction.

[0106] is the variance of the gray level in the y direction.

[0107] Step 2: Calculate the roughness value on the mesh.

[0108] In terms of roughness coefficient assignment, the present invention can achieve distributed and accurate assignment by calculation unit based on remote sensing image analysis of underlying surface characteristics in different regions, overcoming the drawback of traditional unified assignment that does not consider spatial heterogeneity. In terms of model parameter calibration, multi-period remote sensing data is used to obtain water level and other information, which improves the shortcomings of fixed site monitoring data, provides more comprehensive hydrological information and reduces costs.

[0109] (3-3) Steps for calculating unit roughness in non-dry season:

[0110] 1) Classify objects to obtain feature data

[0111] 1.1 Generate a river channel category coding rule table: Classify and assign codes to river channel types and their branches according to the river channel roughness value table to generate a river channel category coding rule table. For example, river channel types and characteristics are divided into small rivers (code 1, including plain rivers and mountain rivers and other branches), large rivers (code 2), and floodplain flooding during floods (code 3, with branches such as grassland, cultivated land, bushes, and trees).

[0112] 1.2 Marking and coding of land object categories: The normalized water index method is used to identify the water body and land object characteristics of the non-dry season river remote sensing image, and the normalized water index NDWI (calculated as Where Green is the reflectance of the green light band, and NIR is the reflectance of the near infrared band) to highlight the water body information, and mark the corresponding land feature category code on the remote sensing image according to the river channel category coding rule table.

[0113] 1.3 Extracting ground feature data: Use the gray level co-occurrence matrix to extract ground feature data from the encoded remote sensing image, select energy (the eigenvalue calculation formula is Where N is the dimension of the gray-level co-occurrence matrix (the number of gray levels), and P(i,j) is the element in the i-th row and j-th column of the gray-level co-occurrence matrix. ij (The formula for calculating the eigenvalue is As feature data of ground objects, energy reflects the uniformity of grayscale distribution in the image, and contrast reflects the strength of contrast between texture primitives.

[0114] 2) Prepare the coding rule table and assign values

[0115] 2.1 Generate a roughness assignment coding rule table: The texture roughness of the remote sensing image is divided based on energy and contrast to obtain a texture-roughness degree coding table (e.g., when the energy is high and the contrast is low, the roughness takes the minimum value and is encoded as 1; when the energy and contrast are average, the roughness takes the normal value and is encoded as 2; when the energy is low and the contrast is high, the roughness takes the maximum value and is encoded as 3), and the roughness assignment coding rule table is generated in combination with the river channel category coding rule table.

[0116] 2.2 Constructing the texture-roughness assignment matrix and calculating the comprehensive roughness value

[0117] 2.2.1 Constructing the grid unit roughness matrix: According to the roughness assignment coding rule table, the roughness value is assigned to each grid unit on the remote sensing image, and arranged in an M×N matrix to form an M×N grid unit roughness matrix R ( Among them, R ij represents the roughness value of the grid cell in the row and column)

[0118] 2.2.2 Determine the grid cell roughness vector R′: Expand the M×N grid cell roughness matrix R into a (M×N)×1 column vector R′

[0119] (R′=(R 11 R 12 … R 1N R 21 R 22 … R 2N … R M1 R M2 … R MN ), each element of R′ represents the roughness value of the grid cell in the row and column).

[0120] 2.2.3 Constructing the overlapping area ratio matrix: The overlapping area ratio of the calculation unit of the hydrodynamic model and the grid unit of the remote sensing image forms an overlapping area ratio matrix A of P×(M×N) ( Where P is the number of hydrodynamic model calculation units, and M×N is the number of grid cells).

[0121] 2.2.4 Calculate the comprehensive roughness value matrix: Through matrix multiplication S k =A·(R′) T , calculate the comprehensive roughness value of each calculation unit, where S k represents the comprehensive roughness value of the kth calculation unit).

[0122] 4. Step 4: Determine the boundary conditions of the river hydrodynamic model (where the initial flow field conditions are assigned using remote sensing images: select the remote sensing image at the start of the model, use its water edge and elevation values ​​to generate a water surface elevation file, and obtain the initial water level of each calculation grid through raster operations.)

[0123] (4-1) Extract the initial water level:

[0124] Use high-resolution remote sensing images to obtain the water edge at the start of the model. Combine the water edge with altimetry satellite data or sparse hydrological station water level data, assign elevation values ​​to the water edge, generate a water surface elevation file, and obtain the initial water level.

[0125] (4-2) Grid initial water level:

[0126] The water surface elevation file is converted into the initial water level value corresponding to the model calculation grid through the raster operation tool. In each calculation grid unit, spatial interpolation is performed according to the water level change trend to assign the initial water level to each grid.

[0127] (4-3) Boundary condition setting:

[0128] Upstream boundary condition: Use flow rate as the boundary condition.

[0129] Flow data can be derived from hydrological station monitoring data or obtained through historical statistical data. At the inflow section, a flow curve over time is set to ensure that the dynamic changes in upstream water volume can be accurately reflected in the model.

[0130] Downstream boundary conditions: The water level data recorded by the downstream section hydrological station is used as the boundary condition.

[0131] 5. Step 5: Select multi-scene high-resolution remote sensing images that cover all or part of the area, extract the water surface vector file, and the altimetry satellite data at the corresponding time, generate the water surface elevation file at the corresponding time, and determine the simulation effect of the hydrodynamic model by comparing the consistency of the water level or the consistency of the water surface vector shape, so as to realize the calibration and verification of the model.

[0132] Specifically, the following parameters are used to calibrate and verify the model:

[0133] Jaccard coefficient, Hausdorff distance, shape similarity, intersection area percentage, Fréchet distance.

[0134] Among them, the Jaccard coefficient is used to measure the area similarity between two polygons, the ratio of the intersection area to the joint area, and is used to quantify the coverage accuracy of water body extraction. The formula is:

[0135]

[0136] Where: A1 and A2 are the areas of the two polygons, A1∩∩A2 is the overlapping area of ​​the two polygons, and A1UA2 is the joint area of ​​the two polygons. The closer the Jaccard coefficient is to 1, the more similar the areas of the two polygons are.

[0137] Hausdorff distance: measures the maximum distance between the boundaries of two polygons. It represents the geometric difference between two shapes by calculating the maximum distance from a point on one polygon to the nearest point on another polygon. The formula is:

[0138] d H (A, B) = max{sup a∈A inf b∈B d(a,b), sup b∈B inf a∈A d(a, b)}

[0139] Where: A and B are the boundary point sets of the two polygons, d(a, b) is the Euclidean distance between the two points, and the smaller the Hausdorff distance, the closer the two polygon boundaries are. This helps to evaluate the accuracy of the extracted boundary.

[0140] Shape similarity: By simplifying the polygon outline into a set of feature points, the shape similarity is calculated based on the average boundary distance of the outline. The formula is:

[0141]

[0142] Where: p i and q i are the corresponding points on the two polygon contours, d(p i ,q i ) is the distance between two points, and n is the number of sampling points. Shape similarity helps to evaluate the shape matching degree of the extraction results of different methods, especially the shape preservation ability extracted under complex terrain.

[0143] Intersection Area Percentage: Measures the ratio of the overlapping area of ​​two polygons to the total area of ​​one of the polygons, and is used to evaluate the degree of overlap of the extracted results. The formula is:

[0144]

[0145] Among them: A1 ∩ A2 is the overlapping area of ​​the two polygons, and A1 is the total area of ​​one of the polygons. The higher the percentage of the intersection area, the greater the overlap between the two polygons, which means the better the coverage completeness of the extraction result.

[0146] Fréchet distance: used to evaluate the similarity of two paths or polygonal contours, which can be compared to the maximum deviation between a person and a dog walking along two paths. The formula is:

[0147]

[0148] Where: Y A and γ B are two paths or polygonal outlines, is a mapping function used to match two paths, and Φ is the set of all continuous monotonically increasing functions. The Fréchet distance is calculated by a dynamic programming algorithm, and the smaller the distance, the closer the two paths or polygon contours are. This indicator is very effective in describing the deviation of contour paths and helps to evaluate the performance of the method in capturing the complex changes of river boundaries. )

[0149] In summary, the present invention has the following advantages:

[0150] 1. Use high-resolution remote sensing data to extract the water edge, and use altimetry satellite data or sparse hydrological station water level data to assign elevation values ​​to the water edge, and then form river channel topography data through elevation interpolation.

[0151] Remote sensing technology shows significant advantages in this method. It can obtain water edge information on the river surface within a larger spatial range, especially in complex or difficult-to-access terrain areas. The altimetry satellite data assigns elevation values ​​to the water edge, which can effectively make up for the shortcomings of ground water level measurement in spatial coverage and provide more extensive and dynamic hydrological data.

[0152] 2. Using remote sensing methods to achieve distributed corresponding assignment of the roughness of the computational grid can better reflect the real situation in nature and achieve accurate simulation of the hydrodynamic process of the river.

[0153] Through high-resolution remote sensing data, this method can conduct a more detailed spatial distribution analysis of the underlying surface type, vegetation coverage and riverbed morphology in different areas of the river channel, so as to accurately assign the roughness coefficient according to the specific conditions of each area in the model calculation.

[0154] 3. Using remote sensing water surface morphology data from different periods of time to input the model boundary conditions (using the extracted water surface vector file to assign initial conditions to the calculation units of the river section hydrodynamic model).

[0155] 4. Use water surface morphology data from different time periods to calibrate and verify model parameters.

[0156] Based on the method of the present invention, the embodiment of the present disclosure also provides a system corresponding to the above method, which includes:

[0157] The waterside acquisition module is used to obtain the remote sensing image of the river, obtain the river water body vector file based on the remote sensing image, and obtain the waterside line;

[0158] The elevation value assignment module is used to obtain the water level data at the water edge of the river channel using the altimetry satellite; according to the acquired water edge and the corresponding water level data, the elevation values ​​are assigned to the corresponding points of the water edge of the straight river section and the curved river section respectively;

[0159] For straight river sections, when the point measured by the altimeter satellite coincides with the river section, the elevation value at the intersection of the altimeter satellite point and the waterside line is taken as the elevation value of the waterside line; if there is an angle between the altimeter satellite and the flow section, the elevation value at the intersection of the altimeter satellite point and the flow section is selected as the elevation value of the point on the waterside line corresponding to the flow section; for curved river sections, linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside line corresponding to the curved river section is obtained based on the fitting relationship;

[0160] The model building module is used to build a digital grid model of the river terrain based on the water edge elevation values ​​of the straight river section and the curved river section; and to build a river hydrodynamic model using the digital grid model of the river terrain;

[0161] The model verification module is used to determine the simulation effect of the hydrodynamic model by comparing the consistency of water level or water surface vector morphology, so as to realize the calibration and verification of the river hydrodynamic model.

[0162] Based on the same inventive concept as the above disclosed content, an embodiment of the present disclosure also provides a prediction device corresponding to the above method, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method.

[0163] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0164] Based on the same inventive concept, the present disclosure also provides a computer storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor performs the above method.

[0165] Although the present disclosure 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; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for constructing a river hydrodynamic model, characterized in that: include: Obtain remote sensing images of the river, obtain river water body vector files based on the remote sensing images, and obtain the water edge line; The water level data at the corresponding water edge of the river channel is obtained by using an altimetry satellite; based on the obtained water edge and the corresponding water level data, the elevation values ​​are respectively assigned to the corresponding points of the water edge of the straight river section and the curved river section; For straight river sections, when the point measured by the altimeter satellite coincides with the river section, the elevation value at the intersection of the altimeter satellite point and the waterside line is taken as the elevation value of the waterside line; if there is an angle between the altimeter satellite and the flow section, the elevation value at the intersection of the altimeter satellite point and the flow section is selected as the elevation value of the point on the waterside line corresponding to the flow section; for curved river sections, linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside line corresponding to the curved river section is obtained based on the fitting relationship; Based on the waterside elevation values ​​of straight river sections and curved river sections, a digital grid model of river terrain is constructed; the river hydrodynamic model is constructed using the digital grid model of river terrain; The simulation effect of the hydrodynamic model is determined by comparing the consistency of water levels or water surface vector shapes, thereby achieving calibration and verification of the river hydrodynamic model.

2. The method for constructing a river hydrodynamic model according to claim 1, characterized in that: The method of constructing a river hydrodynamic model using a digital grid model of river terrain comprises: Based on the reconstructed river terrain, the hydrodynamic model is calculated and gridded to obtain a gridded river terrain model; Calculate the grid roughness of the gridded river terrain model; Determine the boundary conditions of the river hydrodynamic model; A river hydrodynamic model is constructed based on grid roughness, boundary conditions and gridded river terrain model.

3. The method for constructing a river hydrodynamic model according to claim 2, characterized in that: For the curved river section, a linear fitting is performed according to the values ​​of multiple elevation measuring points on the river section, and the elevation value at the waterside corresponding to the curved river section is obtained based on the fitting relationship; Based on the waterside elevation values ​​of straight river sections and curved river sections, a digital grid model of river terrain is constructed; including: Linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section. After the elevation value of the waterside corresponding to the curved river section is obtained based on the fitting relationship, the missing waterside is interpolated to obtain complete elevation data: Get the water edge line of the highest water level as the outer envelope line; Taking the elevation value corresponding to the outer envelope of the water edge as the maximum interpolation range, the elevation value on the water edge is interpolated by the interpolation method to obtain the complete elevation data; Points with equal elevation values ​​are connected to form contour lines, and digital grid models of river terrain are further generated based on these contour lines.

4. The method for constructing a river hydrodynamic model according to claim 2, characterized in that: The calculation of mesh roughness comprises: Based on remote sensing images, the roughness ratios in dry season and non-dry season are calculated respectively; The determination of the dry season roughness rate includes: The color remote sensing image is converted into a grayscale image using the gray-level co-occurrence matrix texture feature extraction method. Determine the grayscale level, select a distance d and direction θ, and for each pixel in the grayscale image, count the frequency of occurrence of the grayscale value pair at the distance d and direction θ to form a co-occurrence matrix P(i, j, d, θ), and extract multiple texture features from the co-occurrence matrix; The roughness value is determined based on various texture characteristics.

5. The method for constructing a river hydrodynamic model according to claim 2, characterized in that: Determining the boundary conditions of the river hydrodynamic model includes: Use remote sensing images to obtain the water edge at the start of the model; assign elevation values ​​to the water edge, generate a water surface elevation file, and obtain the initial water level; For the upstream, flow rate is used as the boundary condition; For the downstream, the water level data recorded by the downstream section hydrological station are used as boundary conditions.

6. The method for constructing a river hydrodynamic model according to claim 4, characterized in that: The various texture features include: The uniformity of grayscale distribution, the contrast of grayscale, the degree of disorder of grayscale distribution, the homogeneity of pixel pairs in the image, and the linear relationship between pixel grayscales.

7. The method for constructing a river hydrodynamic model according to claim 1, characterized in that: The method of determining the simulation effect of the hydrodynamic model by comparing the consistency of water levels or the consistency of water surface vector forms, and realizing the calibration and verification of the river hydrodynamic model, includes: The measured data is used to perform calculation comparison with the data of the hydrodynamic model to achieve parameter calibration and verification of the hydrodynamic model; The measured data and the data of the hydrodynamic model are used for calculation and comparison, and the calculated values ​​used for comparison include: Jaccard coefficient, Hausdorff distance, shape similarity, intersection area percentage, and Fréchet distance.

8. A system for constructing a river hydrodynamic model, characterized in that: include: The waterside acquisition module is used to obtain the remote sensing image of the river, obtain the river water body vector file based on the remote sensing image, and obtain the waterside line; The elevation value assignment module is used to obtain the water level data at the water edge of the river channel using the altimetry satellite; according to the acquired water edge and the corresponding water level data, the elevation values ​​are assigned to the corresponding points of the water edge of the straight river section and the curved river section respectively; For straight river sections, when the point measured by the altimeter satellite coincides with the river section, the elevation value at the intersection of the altimeter satellite point and the waterside line is taken as the elevation value of the waterside line; if there is an angle between the altimeter satellite and the flow section, the elevation value at the intersection of the altimeter satellite point and the flow section is selected as the elevation value of the point on the waterside line corresponding to the flow section; for curved river sections, linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside line corresponding to the curved river section is obtained based on the fitting relationship; The model building module is used to build a digital grid model of the river terrain based on the water edge elevation values ​​of the straight river section and the curved river section; and to build a river hydrodynamic model using the digital grid model of the river terrain; The model verification module is used to determine the simulation effect of the hydrodynamic model by comparing the consistency of water level or water surface vector morphology, so as to realize the calibration and verification of the river hydrodynamic model.

9. A device for constructing a river hydrodynamic model, comprising: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for constructing a river hydrodynamic model as described in any one of claims 1 to 7.

10. A computer storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor is enabled to implement the method according to any one of claims 1 to 7.

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