A river water power model construction method based on multi-source remote sensing

Abstract

The present disclosure relates to the technical field of model construction, and provides a river water dynamics model construction method based on multi-source remote sensing, which comprises the following steps: acquiring a river remote sensing image to obtain a water edge line, acquiring water level data by means of an altimeter satellite, and giving an elevation value to a water edge line corresponding point according to the water edge line and the water level data; for a straight river section, a value is taken according to a specific rule, and for a curved river section, the value is determined according to linear fitting of multiple elevation measuring point values on a river section; based on the elevation values of the water edge lines of the straight and curved river sections, a river terrain digital raster model is further constructed by interpolation along the water edge line, the river water dynamics model is constructed by using the river terrain digital raster model, the simulation effect of the water dynamics model is determined by comparing the water level consistency or the water surface vector form consistency, and the calibration and verification of the river water dynamics model are realized. The present disclosure realizes accurate simulation of the river water dynamics process. The present disclosure can reduce the cost of model parameter acquisition.

Description

Technical Field

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

[0002] River hydrodynamic models are the foundation for simulating river hydrodynamic processes and the associated pollutant transport, and are crucial tools for river management and decision-making. Classic river hydrodynamic model construction requires first conducting river topography surveys, then gridding the river based on the topography, setting model boundary conditions, and calibrating and validating model parameters using existing river level and flow data.

[0003] River channel topography measurements include ground surveying, aerial photogrammetry, and underwater topography. Ground surveying uses equipment such as total stations and levels to manually establish control and measurement points to obtain high-precision topographic data. While highly accurate, it is cumbersome and difficult to implement in complex terrain. Aerial photogrammetry utilizes cameras mounted on drones or aircraft to generate digital elevation models (DEMs) through image processing. This method offers high spatial resolution and rapid operation, but is subject to weather conditions and is relatively expensive. Underwater topography measurement primarily relies on large-section methods at hydrological stations and acoustic Doppler current profilers (ADCPs) aboard unmanned vessels. Hydrological stations regularly measure water depth and current velocity at fixed sections to obtain underwater topographic data. This method is suitable for fixed sites, but its coverage is limited and data updates are infrequent. Unmanned vessels equipped with ADCPs can measure current velocity and depth at various locations and depths, generating detailed underwater topographic maps. This method offers greater flexibility and accuracy, but the equipment is costly and subject to limitations in the water environment.

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

[0005] Model parameter calibration requires actual water level and flow monitoring data. Traditionally, this monitoring data comes from hydrological stations established at fixed sections, where flow and water levels are regularly recorded manually or through automated equipment. However, this fixed-site monitoring method suffers from limited data coverage and low update frequency, making it difficult to obtain comprehensive hydrological information, especially in areas with complex river morphology or frequent human activity. Furthermore, 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. It adopts remote sensing images and altimetry satellite data, obtains water edges, assigns elevation values ​​to different river points, and constructs terrain and hydrodynamic models. It solves the problems of limited coverage, low update frequency and high cost of traditional underwater topography measurement.

[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] Using altimetry satellites to obtain water level data at the corresponding waterside of the river; assigning elevation values ​​to corresponding points on the waterside of straight and curved river sections based on the obtained waterside and corresponding water level data;

[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 is taken as the elevation value of the waterside; 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 corresponding to the flow section; for curved river sections, a linear fit is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the corresponding waterside of the curved river section is obtained based on the fitting relationship;

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

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

[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] Furthermore,

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

[0022] Linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section. After obtaining the elevation value of the waterside corresponding to the curved river section based on the fitting relationship, interpolation is performed on the missing parts of the waterside 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 waterline as the maximum interpolation range, the elevation values ​​on the waterline are interpolated using the interpolation method to obtain complete elevation data;

[0025] Points with equal elevation values ​​are connected to form contour lines, and a digital raster model of the river terrain is further generated based on these contour lines.

[0026] Furthermore,

[0027] The calculation of mesh roughness includes:

[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 ratio 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 number of grayscale levels, select a distance d and direction θ, and for each pixel in the grayscale image, count the frequency of occurrence of its grayscale value pair at 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] Furthermore,

[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, the 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] Furthermore,

[0039] The multiple 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] Furthermore,

[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] Utilize the measured data and the data of the hydrodynamic model to perform calculation comparison and realize the 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 water edge acquisition module is used to obtain remote sensing images of the river, obtain the river water body vector file based on the remote sensing image, and obtain the water edge;

[0047] The elevation value assignment module is used to obtain the water level data at the corresponding water edge of the river using the altimetry satellite; based on 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 is taken as the elevation value of the waterside; 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 corresponding to the flow section; for curved river sections, a linear fit is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the corresponding waterside of 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 straight river sections and curved river sections; 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 levels or the consistency of water surface vector shapes, thereby realizing the calibration and verification of the river hydrodynamic model.

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

[0052] The present invention acquires river channel remote sensing images and altimetry satellite water level data, uses remote sensing images to observe the river channel surface over a larger spatial range to expand coverage, and updates data more flexibly based on factors such as satellite revisit cycles. This makes up for the limited coverage and low update frequency of hydrological stations, while avoiding the high cost and environmental restrictions of deploying equipment in waters when unmanned boats carry ADCPs.

[0053] In terrain construction, for straight river channels, elevation values ​​are determined based on the intersection of altimetry satellite measurement points with the waterline or flow section. Directly utilizing measurement data ensures accuracy, allowing reliable elevations to be obtained regardless of the relationship between the measurement points and the section, providing precise foundational data for digital grid model construction. For curved river channels, which experience a lateral gradient due to centrifugal force, the elevation at the waterline is determined through linear fitting of multiple elevation measurement points. This method, based on the changing patterns of curved terrain, captures gradual elevation changes and makes the model more realistic. Specific methods are used to assign elevation values ​​to straight and curved river channels, fully accounting for the characteristics of the river's morphology. The resulting digital grid model more accurately reflects the underwater topography and is more comprehensive and accurate than large-section methods at hydrological stations and unmanned vessel measurements.

[0054] Other features and advantages of the present disclosure will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present disclosure. The purposes and other advantages of the present disclosure can be realized and obtained by the structures indicated 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall 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 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 for 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 point) and the minimum area water edge (the boundary between the water body and the land boundary when the water level reaches the lowest point);

[0062] Specifically:

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

[0064] In these ground tracks, the left and right blips within each pair are approximately 90 meters apart in the cross-track direction and approximately 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 the left and right blips, 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 measurement of waterside elevations in 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, obtain the water level data of the altimetry satellite at the corresponding time;

[0067] (1-3) Assign elevation values ​​to corresponding points on the waterside line in straight river sections and curved river sections, and use interpolation 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 the altimeter is at an angle to the flow section, select the elevation at the intersection of the altimeter's measurement point and the flow section. This intersection represents the elevation point on the flow section. Simply assign the elevation at that intersection to the corresponding point on the waterline of the flow section, thus obtaining the elevation values ​​for both sides of the section.

[0071] Bend of the river:

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

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

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

[0075] The interpolated elevation data is 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 channel topography reconstructed in step 1 is used to generate a mesh for the hydrodynamic model. Because the wall surface is affected by viscosity, it is densified using a structured mesh. The radial boundaries are densified, and the complex flow structure at the bend requires densification. The resulting river channel computational mesh is then generated.

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

[0079] Specifically, they include:

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

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

[0082] During the dry season, water flows within the river channel, and the terrain within the water surface is 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 many gravels, pebbles, or large boulders on the riverbed. The roughness coefficient of this river section is high. If the water surface has no obvious texture characteristics, it indicates that the riverbed is relatively smooth, usually bedrock or a stone riverbed. The roughness coefficient of this river section is low. Table 1 shows the corresponding roughness values ​​of the riverbed material:

[0083] Table 1 Corresponding riverbed material roughness values

[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 number of grayscale levels (for example, 256 levels). A distance d and direction θ (0°, 45°, 90°, 135°) are selected. For each pixel, the frequency of occurrence of its grayscale value pairs at distance d and direction θ is counted to form a co-occurrence matrix P(i, j, d, θ). Various texture features are extracted from the co-occurrence matrix.

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

[0088] a. Energy E: This represents the uniformity of the grayscale distribution. A higher energy indicates a more regular and uniform texture pattern. Value range: 0 to 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 contrast between texture primitives. The higher the contrast, the rougher the image. 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: This represents the uncertainty and disorder of grayscale distribution and is a measure of image complexity. Its value range is greater than 0 and has no fixed upper limit.

[0095] The larger the entropy, the more complex the image, the more chaotic 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. Higher values ​​indicate smoother textures. Values ​​range from 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, this invention uses remote sensing imagery to analyze underlying surface characteristics across different regions, enabling distributed and precise assignment based on computational units. This overcomes the drawback of traditional uniform assignments that ignore spatial heterogeneity. For model parameter calibration, multi-period remote sensing data is used to obtain information such as water levels, overcoming the shortcomings of fixed-site monitoring data, providing more comprehensive hydrological information and reducing 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 classification coding rule table: Classify and assign codes to river channel types and their tributaries based on the river channel roughness value table to generate a river channel classification coding rule table. For example, river channel types and characteristics can be categorized as small rivers (code 1, including tributaries such as plain rivers and mountain rivers), large rivers (code 2), and floodplain floodplains (code 3, including tributaries such as grassland, cultivated land, shrubs, 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 (calculation formula is Green is the reflectance of the green light band, and NIR is the reflectance of the near-infrared band) to highlight water body information, and the corresponding land feature category code is marked on the remote sensing image according to the river channel category coding rule table.

[0113] 1.3 Extracting ground feature data: Use 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 characteristic value is As feature data of ground objects, energy reflects the uniformity of grayscale distribution of 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 (for example, when the energy is high and the contrast is low, the roughness takes the minimum value and is coded as 1; when the energy and contrast are the average, the roughness takes the normal value and is coded as 2; when the energy is low and the contrast is high, the roughness takes the maximum value and is coded as 3). The roughness assignment coding rule table is then 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 remote sensing image grid unit 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 (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 use raster operations to obtain the initial water level of each calculation grid.)

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

[0124] Use high-resolution remote sensing imagery to obtain the water edge at the model's initial time. Combine this 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) Gridded initial water level:

[0126] Using raster operation tools, the water surface elevation file is converted into the initial water level value corresponding to the model calculation grid. In each calculation grid cell, spatial interpolation is performed based on the water level change trend to assign an initial water level to each grid cell.

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

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

[0129] Flow data can be obtained from hydrological station monitoring data or historical statistical data. At the inflow section, a flow curve is set up 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-view high-resolution remote sensing images covering 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 water levels or the consistency of water surface vector shapes to achieve model calibration and verification.

[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 area of ​​their overlap, and A1UA2 is the union 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 expresses the geometric difference between two shapes by calculating the maximum distance from a point on one polygon to the nearest point on the other 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 points of the two polygons, and d(a, b) is the Euclidean distance between the two points. The smaller the Hausdorff distance, the closer the two polygon boundaries are. This helps 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 They 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 degree of shape matching between 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, used to evaluate the degree of overlap of the extracted results. The formula is:

[0144]

[0145] Where A1∩A2 is the overlapping area of ​​the two polygons, and A1 is the total area of ​​one of the polygons. A higher percentage of intersection area indicates a greater degree of overlap between the two polygons, indicating better coverage completeness of the extraction results.

[0146] Fréchet distance: used to evaluate the similarity between two paths or polygon outlines. It 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 using a dynamic programming algorithm; smaller values ​​indicate closer alignment between two paths or polygonal outlines. This metric is very effective in describing deviations between outline paths and helps evaluate how well a method captures the complex variations in 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 the water edge elevation value, and then form the river channel topography data through elevation interpolation.

[0151] Remote sensing technology demonstrates significant advantages in this method, enabling the acquisition of river surface water edge information over a larger spatial range. This is particularly true in areas with complex or inaccessible terrain. Assigning elevation values ​​to water edges using altimetry satellite data can effectively compensate for the shortcomings of surface water level measurements in spatial coverage, providing 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] This method uses high-resolution remote sensing data to conduct a more detailed spatial distribution analysis of the underlying surface type, vegetation cover and riverbed morphology in different areas of the river, 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 time periods 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 the model parameters.

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

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

[0158] The elevation value assignment module is used to obtain the water level data at the corresponding water edge of the river using the altimetry satellite; based on 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 is taken as the elevation value of the waterside; 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 corresponding to the flow section; for curved river sections, a linear fit is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the corresponding waterside of 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 straight river sections and curved river sections; 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 levels or the consistency of water surface vector shapes, thereby realizing 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-mentioned 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-mentioned 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 further provides a computer storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to perform 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 cause the essence of the corresponding technical solutions to 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; Using altimetry satellites to obtain water level data at the corresponding waterside of the river; assigning elevation values ​​to corresponding points on the waterside of straight and curved river sections based on the obtained waterside and corresponding water level data; 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 is taken as the elevation value of the waterside; 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 corresponding to the flow section; for curved river sections, a linear fit is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the corresponding waterside of the curved river section is obtained based on the fitting relationship; Based on the waterside elevation values ​​of straight and curved river sections, a digital grid model of the river terrain is constructed; the river hydrodynamic model is constructed using the digital grid model of the river terrain; The simulation effect of the hydrodynamic model is determined by comparing the consistency of water levels or water surface vector shapes, thus achieving calibration and verification of the river hydrodynamic model. The method of constructing a river hydrodynamic model using a digital grid model of river terrain includes: 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; Construct a river hydrodynamic model based on grid roughness, boundary conditions, and a gridded river terrain model; The calculation of mesh roughness includes: Based on remote sensing images, the roughness ratios in the dry season and the non-dry season are calculated respectively; the determination of the roughness ratio in the dry season 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 number of gray levels, 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 , extracting multiple texture features from the co-occurrence matrix; The roughness value is determined based on various texture characteristics.

2. The method for constructing a river hydrodynamic model according to claim 1, characterized in that: For the curved river section, a linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the waterside corresponding to the curved river section is obtained based on the fitting relationship; Based on the waterside elevation values ​​of straight and curved river sections, a digital grid model of the river terrain is constructed; including: Linear fitting is performed based on the values ​​of multiple elevation measurement points on the river section. After obtaining the elevation value of the waterside corresponding to the curved river section based on the fitting relationship, interpolation is performed on the missing parts of the waterside 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 waterline as the maximum interpolation range, the elevation values ​​on the waterline are interpolated using the interpolation method to obtain complete elevation data; Points with equal elevation values ​​are connected to form contour lines, and a digital raster model of the river terrain is further generated based on these contour lines.

3. The method for constructing a river hydrodynamic model according to claim 1, 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, the 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.

4. The method for constructing a river hydrodynamic model according to claim 1, characterized in that: The multiple 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.

5. 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: Utilize the measured data and the data of the hydrodynamic model to perform calculation comparison and realize the 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.

6. A system for constructing a river hydrodynamic model, characterized in that: include: The water edge acquisition module is used to obtain remote sensing images of the river, obtain the river water body vector file based on the remote sensing image, and obtain the water edge; The elevation value assignment module is used to obtain the water level data at the corresponding water edge of the river using the altimetry satellite; based on 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 is taken as the elevation value of the waterside; 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 corresponding to the flow section; for curved river sections, a linear fit is performed based on the values ​​of multiple elevation measurement points on the river section, and the elevation value of the corresponding waterside of 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 straight river sections and curved river sections; and to build a river hydrodynamic model using the digital grid model of the river terrain; The method of constructing a river hydrodynamic model using a digital grid model of river terrain includes: 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; Construct a river hydrodynamic model based on grid roughness, boundary conditions, and a gridded river terrain model; The calculation of mesh roughness includes: Based on remote sensing images, the roughness ratios in the dry season and the non-dry season are calculated respectively; the determination of the roughness ratio in the dry season 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 number of gray levels, 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 , extracting multiple texture features from the co-occurrence matrix; Determining a roughness value based on multiple texture features; The model verification module is used to determine the simulation effect of the hydrodynamic model by comparing the consistency of water levels or the consistency of water surface vector shapes, thereby realizing the calibration and verification of the river hydrodynamic model.

7. 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 5.

8. 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 5.

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