River terrain reconstruction method and device based on energy loss and genetic algorithm coupling

Abstract

The application belongs to the technical field of hydrological geomorphology, and discloses a river channel terrain reconstruction method and device based on energy loss and genetic algorithm coupling, which comprises the following steps: collecting remote sensing images, hydrological station measured data and elevation data of the river channel; determining a water surface width sequence of the river channel flow section based on the river channel water surface vector of the remote sensing images; analyzing the measured data near the river channel to determine the corresponding hydraulic parameters of the river channel flow section; determining a target width-depth ratio sequence of the river channel flow section by using the genetic algorithm based on the hydraulic parameters and taking the minimum energy loss as an optimization target; constructing the underwater terrain of the river channel based on the water surface width sequence and the target width-depth ratio sequence of the river channel flow section; and fusing the underwater terrain and the elevation model data to complete the reconstruction of the river channel terrain. The scheme can reconstruct a more accurate river channel terrain based on the measured data and optical remote sensing images of the river channel, and considering the characteristics of various hydraulic parameters corresponding to the flow section and the energy loss.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrological geomorphology, and in particular to a river channel terrain reconstruction method and device based on energy loss and genetic algorithm coupling. BACKGROUND

[0002] Underwater terrain data is a key boundary condition for modeling hydrodynamic processes, which can affect the predictive ability of the model. Under the current hydrodynamic theory framework, the model can accept various boundary conditions as input, but when describing the underwater terrain, the model pays more attention to the geometric and physical properties of the terrain. To accurately capture the characteristics of the underwater terrain, some researchers use publicly available global digital elevation model (DEM) data to obtain the noise value of the elevation in the river channel range after sliding average optimization as the underwater terrain. The deficiency of this method is that the noise value of the river channel is affected by many factors, and it cannot form a consistent correlation with the river bottom terrain. Although the results obtained meet the calculation of the hydrodynamic model under certain conditions, they lose consistency with the real river terrain. Therefore, the method of reconstructing the river terrain needs to be improved. Some researchers reconstruct the riverbed digital terrain based on SRTM DEM data to solve the problem of insufficient DEM data in describing the riverbed terrain. They use a strong local weighted regression algorithm to smooth the river network profile elevation extracted from the DEM data to eliminate abnormal elevation values in the original data. Using river surface polygon data as a mask, the inverse distance weighted interpolation method is used to interpolate the unknown elevation pixels inside the river surface to obtain a three-dimensional digital river surface. By combining the river surface elevation and water depth information, the riverbed cross-sectional shape is assumed, and the three-dimensional digital terrain reconstruction of the riverbed area is completed. However, for river sections without water depth information, the digital terrain cannot be reconstructed using this method. SUMMARY

[0003] To solve the above problems, the present application provides a river terrain reconstruction method and device based on energy loss and genetic algorithm coupling, which uses measured data and optical remote sensing images of the river channel as data basis, considers the characteristics of various water conservancy parameters corresponding to the flow section and energy loss, and can reconstruct more accurate river terrain.

[0004] To achieve the above purpose, the present application adopts the following technical solutions:

[0005] In a first aspect, the present application provides a river terrain reconstruction method based on energy loss and genetic algorithm coupling, comprising:

[0006] Collecting optical remote sensing images of the river channel, hydrological station measured data near the river channel, and elevation model data of the river channel;

[0007] Determining the water surface width sequence of the river flow section based on the river water surface vector of the optical remote sensing image;

[0008] The measured data of the hydrological station near the river is analyzed to determine the corresponding hydraulic parameters of the river flow section;

[0009] Based on the corresponding hydraulic parameters of the river flow section, the genetic algorithm is used to determine the target width-depth ratio sequence of the river flow section with the optimization target of minimum energy loss;

[0010] Based on the water surface width sequence of the river flow section and the target width-depth ratio sequence of the river flow section, the underwater topography of the river is constructed;

[0011] The underwater topography of the river is fused with the elevation model data of the river to complete the reconstruction of the river topography.

[0012] Further, the measured data of the hydrological station near the river is analyzed to determine the corresponding hydraulic parameters of the river flow section, including:

[0013] The measured section data in the measured data of the hydrological station is analyzed to determine the fitting model of the river flow section shape;

[0014] The river flow section is fitted through the shape coefficient of the fitting model, and the river flow section shape is generalized to a geometric shape;

[0015] Based on the area formula of the geometric shape river flow section, the hydraulic radius of the river flow section is determined;

[0016] Based on the hydraulic radius of the river flow section and the Manning formula, the flow velocity of the river flow section is determined.

[0017] Further, based on the corresponding hydraulic parameters of the river flow section, the genetic algorithm is used to determine the target width-depth ratio sequence of the river flow section with the optimization target of minimum energy loss, including:

[0018] Based on the corresponding hydraulic parameters of the adjacent flow sections in the river flow section, the Darcy-Weisbach algorithm is used to determine the energy loss of the adjacent flow sections;

[0019] Based on the energy loss of the adjacent flow sections, the Bernoulli equation is used to determine the theoretical water level of the adjacent flow sections;

[0020] An initial width-depth ratio sequence of the river flow section is obtained;

[0021] Based on the initial width-depth ratio, the terrain factor constraint, the width-depth ratio range constraint, and the flow continuity constraint, the genetic algorithm is used to determine the target width-depth ratio sequence of the river flow section with the optimization target of minimum energy loss.

[0022] Further, based on the water surface width sequence of the river flow section and the target width-depth ratio sequence of the river flow section, the underwater topography of the river is constructed, including:

[0023] determine the water depth and the river bottom elevation of all the flow cross sections in the river channel based on the water surface width sequence of the flow cross sections of the river channel and the target width-depth ratio sequence of the flow cross sections of the river channel;

[0024] interpolate the river bottom elevation based on the cross section shape of the flow cross section, and construct the underwater topography of the river channel.

[0025] In a second aspect, the present application further provides a device for reconstructing river channel topography based on energy loss and genetic algorithm coupling, comprising:

[0026] a collection module configured to collect optical remote sensing images of the river channel, measured data of hydrological stations near the river channel, and elevation model data of the river channel;

[0027] a determination module configured to determine a water surface width sequence of the flow cross sections of the river channel based on the river channel water surface vector of the optical remote sensing images;

[0028] an analysis module configured to analyze the measured data of the hydrological stations near the river channel, and determine corresponding hydraulic parameters of the flow cross sections of the river channel;

[0029] a calculation module configured to determine a target width-depth ratio sequence of the flow cross sections of the river channel based on the corresponding hydraulic parameters of the flow cross sections of the river channel, and adopt a genetic algorithm to minimize energy loss as an optimization target;

[0030] a construction module configured to construct an underwater topography of the river channel based on the water surface width sequence of the flow cross sections of the river channel and the target width-depth ratio sequence of the flow cross sections of the river channel;

[0031] a fusion module configured to fuse the underwater topography of the river channel with the elevation model data of the river channel, and complete reconstruction of the river channel topography.

[0032] Further, the analysis module is further configured to:

[0033] perform generalization analysis on the measured cross section data in the measured data of the hydrological stations, and determine a fitting model of the flow cross section shape of the river channel;

[0034] fit the flow cross section of the river channel through the shape coefficient of the fitting model, and generalize the flow cross section shape of the river channel into a geometric shape;

[0035] determine a hydraulic radius of the flow cross section of the river channel based on an area formula of the geometric shape flow cross section of the river channel;

[0036] determine a flow velocity of the flow cross section of the river channel based on the hydraulic radius of the flow cross section of the river channel and a Manning formula.

[0037] Further, the calculation module is further configured to:

[0038] Based on the hydraulic parameters corresponding to adjacent flow sections in the river flow section, the energy loss of the adjacent flow sections is determined by using the Darcy-Weisbach algorithm.

[0039] Based on the energy loss of the adjacent flow sections, the theoretical water level of the adjacent flow sections is determined by using the Bernoulli equation.

[0040] An initial width-depth ratio sequence of the river flow section is obtained.

[0041] Based on the initial width-depth ratio, the terrain factor constraint, the width-depth ratio range constraint and the flow continuity constraint, a target width-depth ratio sequence of the river flow section is determined by using the genetic algorithm with the minimum energy loss as the optimization target.

[0042] Further, the construction module is further used for:

[0043] Based on the water surface width sequence of the river flow section and the target width-depth ratio sequence of the river flow section, the water depth and the river bottom elevation of all flow sections in the river flow section are determined.

[0044] Based on the section shape of the flow section, the river bottom elevation is interpolated to construct the underwater terrain of the river.

[0045] In a third aspect, the present application further provides an electronic device, comprising a processor and a memory.

[0046] The processor is coupled with the memory.

[0047] The processor is used for reading and executing the program or instruction stored in the memory, so that the device executes the method of the first aspect.

[0048] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the program is executed by the processor to realize the method of the first aspect.

[0049] The technical solution provided by the present application has at least the following technical effects or advantages:

[0050] The technical scheme of the present application calculates the hydrological parameters corresponding to the river channel topography section based on the measured data of the hydrological station, that is, the geometric shape of the section and the corresponding power function model are determined by generalizing and analyzing the section of the river bottom, and the hydrological parameters corresponding to the section of the river channel topography are determined according to the area formula of the section shape combined with the shape coefficient in the power function model. The target width-depth ratio of the flow section is determined based on the hydraulic parameters and the energy loss of the flow section, and the river bottom elevation is determined combined with the water surface width extracted from the optical remote sensing image, and the underwater topography of the river bottom is constructed according to the shape of the flow section and the river bottom elevation, and the reconstruction of the river channel topography is completed. The scheme of the present application takes the measured data and optical remote sensing image of the river channel as the data basis, considers the characteristics of the corresponding multiple water conservancy parameters and energy loss of the flow section, and can reconstruct a more accurate river channel topography.

[0051] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0053] Figure 1 The flowchart of a river channel topography reconstruction method based on energy loss and genetic algorithm coupling in an embodiment of the present application;

[0054] Figure 2 The schematic diagram of a common river flow section shape in an embodiment of the present application;

[0055] Figure 3 The schematic diagram of a hypothetical flow section shape in an embodiment of the present application;

[0056] Figure 4 The schematic diagram of a triangular section for generalizing the shape of a river flow section in an embodiment of the present application;

[0057] Figure 5 The schematic diagram of a power function section for generalizing the shape of a river flow section in an embodiment of the present application;

[0058] Figure 6 The structural schematic diagram of a river channel topography reconstruction device based on energy loss and genetic algorithm coupling in an embodiment of the present application;

[0059] Figure 7 Figure 1 is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0061] Figure 1 The method is a river channel topography reconstruction method based on coupling of energy loss and genetic algorithm, as shown in the figure, and the method comprises the following steps:

[0062] The technical solutions of the present application will be further described below:

[0063] 1. Collecting optical remote sensing images of the river channel, measured data of hydrological stations near the river channel, and elevation model data of the river channel;

[0064] (1) Collecting high-resolution optical remote sensing images of the river channel, including but not limited to Landsat series, Sentinel series, lock eye series, and high-resolution satellite remote sensing images;

[0065] (2) Obtaining elevation model (DEM) data of the river channel from public global digital elevation model (DEM) data by cropping;

[0066] (3) Collecting measured data of hydrological stations near the river channel, including measured cross-section data, daily flow data, and daily water level data of the river channel;

[0067] (4) Collecting field measured data of the river channel, including unmanned aerial vehicle aerial survey images and underwater topographic data collected by an acoustic Doppler current profiler (ADCP) carried by an unmanned ship, for verifying the accuracy of the optical remote sensing data and the measured data of the hydrological stations.

[0068] 2. Determining a water surface width sequence of the river channel flow section based on a river channel water surface vector of optical remote sensing images;

[0069] The optical remote sensing images of the river channel are imported into a commonly used remote sensing image processing tool, radiation calibration and atmospheric correction are performed on the optical remote sensing images, and a river channel water surface vector is extracted; the river channel water surface vector is imported into a geographic information system tool, and a river channel center line is determined. The river channel water surface vector can be obtained by using a water body index method and the like.

[0070] The stable flow river channel is a river section with constant flow, relatively fixed riverbed morphology and no significant external interference in a certain time, and the stable flow value of the stable flow river channel is convenient for calculation of subsequent hydraulic parameters.

[0071] The migration parameters of the river center line, such as migration distance, migration speed and migration acceleration, are obtained by using a geographic information system tool, the migration parameters are subjected to cluster analysis, the relative stability index RSIRR-G (river section relative stability index) of the entire river is calculated, if the value of the relative stability index RSIRR-G meets the specified threshold requirement, the river channel is a stable flow river channel, and the river channel terrain reconstruction in the application refers to terrain reconstruction of the stable river section.

[0072] The flow section refers to a cross section perpendicular to the direction of river flow, and the river flow section refers to a sequence of all flow sections.

[0073] The water surface width w of each flow section in the river flow section is calculated by using the 'create perpendicular line' tool of the geographic information system tool along the center line and superimposing the water surface vector, and then the water surface width sequence of the river flow section is determined.

[0074] 3. The measured data of the hydrological station near the river channel is analyzed to determine the corresponding hydraulic parameters of the river flow section.

[0075] The hydraulic parameter data of the river flow section can reflect the motion characteristics of the water flow in a specific terrain, including section morphology, hydraulic radius, wet perimeter, water flow velocity and the like.

[0076] The measured data of the hydrological station near the river channel is used as the data source for the river channel terrain reconstruction, that is, the section data in the measured data of the hydrological station can be regarded as the data source of the river flow section. The measured section data in the measured data of the hydrological station is subjected to generalization analysis to determine the fitting model of the river flow section morphology, and the model can be expressed as:

[0077]

[0078] In the formula, y represents the water depth of the flow section, x represents the water surface width of the flow section, and γ represents the morphology coefficient.

[0079] The river flow section is fitted by the morphology coefficient of the fitting model, and the river flow section morphology is generalized to a geometric morphology. When the morphology coefficient γ = 1, the river flow section can be generalized to a triangular section; when the morphology coefficient γ = 2, the river flow section can be generalized to a standard power function section; when γ takes other values, corresponding other geometric morphologies are obtained; the generalized river flow section morphology has a determined morphology coefficient ​and γ, indicating a defined fitting model. All geometric forms corresponding to the generalized river flow section can be collectively referred to as power function forms. The hydraulic radius of the river flow section is determined based on the area formula of the geometric river flow section. The flow velocity of the river flow section is determined based on the hydraulic radius of the river flow section and the Manning formula. The geometric form of the generalized river flow section is the cross-sectional form in the hydraulic parameters.

[0080] The details are as follows:

[0081] The cross section of a river channel is usually V-shaped. Due to the large longitudinal gradient, fast flow velocity and strong scouring effect, the water depth, water surface width and cross-sectional area will fluctuate with the change of flow (see Figure 2 (a)). In mountainous and plain rivers, the river channel is affected by the topography and geological conditions, and the cross section often presents asymmetric characteristics, with one side slope being steep and the other side being gentler (see Figure 2 (b)), such cross-sections can be generalized as triangles; for rivers in plain and hilly areas, due to the small longitudinal gradient and slow flow rate, the cross-section shape is characterized by a large width-to-depth ratio (see Figure 2 (c) and (d)). When there are significant topographic protrusions in the river channel, the cross-sectional morphology becomes more complex (see Figure 2 (e)).

[0082] Figure 2 (a) (b) can be roughly generalized into a triangle, the difference is that, Figure 2 (a) is bilaterally symmetrical, Figure 2 (b) is a special case. Therefore, Figure 2 (a)(b) can be generalized into a triangular cross section for analysis. Figure 2 For the wide and shallow river channels shown in (c), (d), and (e), the relationship between the water surface width and the water depth can be approximately generalized into a power function model, and the function form is:

[0083]

[0084] In the formula, y represents the water depth of the flow section; x represents the water surface width of the flow section; and γ represent the morphology coefficient.

[0085] Assume that the water surface width of the flow section is 100m and the water depth is 10m. When γ and γ take the values ​​given in Table 1, the cross-sectional shape is as follows Figure 3 As shown. It can be seen that according to the power function With the given The cross-sectional shape drawn by the and γ values ​​is similar to some wide and shallow river sections in actual projects. Therefore, the wide and shallow sections in the project can be fitted using the power function, but the coefficients are and the exponent γ will change with the cross-sectional size.

[0086] Based on the river flow calculation formula and the known function relationship between the cross-section water surface width, water depth, flow velocity and flow, further analysis of the assumed flow cross-section can determine that any cross-section including triangle can be fitted by power function model;

[0087] River flow calculation formula:

[0088] Q = whv (3-1)

[0089] In the formula, Q is the flow of the flow cross-section, unit m 3 / s; w is the flow cross-section width, unit m; h is the flow cross-section water depth, unit m; v is the flow cross-section flow velocity, unit: m / s

[0090] The function relationship between the water surface width, cross-section average water depth and average flow velocity and flow:

[0091] w = aQ b

[0092] d = cQ f

[0093] v = kQ m (3-2)

[0094] In the formula, a, c and k are coefficients; b, f and m are exponents.

[0095] (3-2) into (3-1), to meet the following relationship:

[0096] b + f + m = 1

[0097] ack = 1 (3-3)

[0098]

[0099] In the formula, y represents the flow cross-section water depth; x represents the flow cross-section water surface width; And γ represents the shape coefficient.

[0100] Table 1

[0101]

[0102] When γ = 1, the cross-section equation is At this time, the flow cross-section is triangular cross-section, and the exponents are b = 0.375, f = 0.375, m = 0.25.

[0103] When γ = 2, the river cross-section is parabolic, and the exponents b, f and m are 0.23, 0.46 and 0.31 respectively.

[0104] Based on the measured data of hydrological station, the shape coefficient of fitting model is adjusted to fit the flow section, and the flow section shape is generalized as geometric shape. The generalized flow section shape has a certain shape coefficient and γ, i.e. a certain fitting model. Exemplarily, Figure 2 The flow section in the equation is the common section shape, which is generalized as Figure 2 (a) (b) to Figure 4 the triangular section shown in the figure; the wide and shallow river channel shown in Figure 2 (c) (d) (e) is generalized as Figure 5 the power function section shown in the figure.

[0105] Triangular section:

[0106] The calculation formula of the flow section area A is:

[0107]

[0108] In the formula, w is the flow section width, unit: m, h is the maximum water depth of the triangular flow section, unit: m;

[0109] The calculation formula of the flow section wet perimeter χ is:

[0110]

[0111] In the formula, χ: flow section wet perimeter, unit: m; θ1 and θ2 are two angles of the triangular section top angle.

[0112] The calculation formula of the hydraulic radius R is:

[0113]

[0114] In the formula, R: hydraulic radius, unit: m; w is the flow section width, unit: m; θ1 and θ2 are two angles of the triangular section top angle.

[0115] Manning formula:

[0116]

[0117] In the formula, v: flow section flow velocity; R: hydraulic radius, unit: m; J is the hydraulic slope; n is the Manning roughness.

[0118] The flow section flow velocity is:

[0119]

[0120] Power function section:

[0121] The calculation formula of the flow section area A is

[0122]

[0123] where w is the water surface width of the flow section, in meters.

[0124] For a wide and shallow river, the wet perimeter χ is approximately equal to the river width w, and thus the formula for the hydraulic radius is:

[0125]

[0126] From the Manning formula, v∝R 2 / 3 , and thus the average flow velocity v∝w 2 / 3 ;

[0127]

[0128] 4. Based on the hydraulic parameters corresponding to the flow section of the river channel, the genetic algorithm is used to determine the target width-depth ratio sequence of the flow section of the river channel, with the minimum energy loss as the optimization objective;

[0129] The flow section of the river channel refers to a sequence of all flow sections. Based on the hydraulic parameters corresponding to adjacent flow sections in the flow section of the river channel, the Darcy-Weisbach algorithm is used to determine the energy loss of the adjacent flow sections. Based on the energy loss of the adjacent flow sections, the Bernoulli equation is used to determine the theoretical water level of the adjacent flow sections. The initial width-depth ratio sequence of the flow section of the river channel is obtained. Based on the initial width-depth ratio, the terrain factor constraint, the width-depth ratio range constraint, and the water flow continuity constraint, the genetic algorithm is used to determine the target width-depth ratio sequence of the flow section of the river channel, with the minimum energy loss as the optimization objective;

[0130] Specifically as follows:

[0131] The total energy loss calculated by the Darcy-Weisbach formula refers to the sum of the friction head loss h f and the local head loss h j .

[0132] The Darcy-Weisbach formula calculates the friction head loss h f between two adjacent sections:

[0133]

[0134] The Chezy coefficient is:

[0135]

[0136] where v is the average flow velocity of the adjacent two sections, in meters per second; v is the average Chezy coefficient of the adjacent two sections, is the average hydraulic radius of two adjacent sections, L is the length of the river center line between two sections, unit: m.

[0137] The local head loss between two adjacent sections is:

[0138]

[0139] In the formula, v i , v i+1 The average flow velocity between two adjacent sections, unit: m / s; g is the acceleration of gravity. Unit: m 2 / s; ζ is the local head loss coefficient, based on the "Hydraulics Calculation Manual", the value of the contraction section is generally 0; in the diffusion section, because v2

[0140] The total energy loss is obtained by combining (3-12) and (3-14):

[0141] h w = h f + h j (3-15)

[0142] The theoretical water level along the river is calculated by using Bernoulli equation, Bernoulli equation:

[0143]

[0144] In the formula, α is the kinetic energy correction coefficient, which is 1.01 here; Z i , Z i+1 The water level of two adjacent sections; v i , v i+1 The average flow velocity of two adjacent sections; h w is the total energy loss.

[0145] In which, v i , v i+1 The average flow velocity of two adjacent sections is calculated:

[0146] v i = Q / A i (3-17)

[0147] In the formula, v i is the flow velocity of the flow section, unit: m / s; Q is the flow of the flow section, because it is a steady flow river, the flow is constant, unit: m 3 / s; A i The area of the flow section of each section, unit: m 2

[0148] The initial width-depth ratio sequence of the river flow section is obtained, and the specific process is as follows:

[0149] Based on the hydrological data near the river, the CAD is used to draw each flow section and the water level closed region graph in the river flow section, and the closed area A0 and the flow section width w0 can be directly obtained through the calculation tool. Combined with the determined flow section shape, i.e. the triangular section or the power function type section, the initial water depth h0 of the flow section is calculated, and the initial width-depth ratio λ0 is further calculated, which can be expressed as: λ0=w0 / h0. The initial width-depth ratio of all flow sections in the river flow section is obtained by calculation.

[0150] Taking the initial width-depth ratio sequence of the river flow section as the initial reference point, the genetic algorithm is introduced to optimize the width-depth ratio to gradually converge to the global optimal solution. The minimum energy loss is taken as the objective function, and the flow continuity and topographic constraint factor are taken as the constraint condition to obtain the width-depth ratio sequence that is most suitable for the water dynamic characteristics of the given river section, i.e. the target width-depth ratio sequence of the river flow section.

[0151] The main steps of the genetic algorithm include:

[0152] a. Initialization of population: a group of solutions (population) is randomly generated, and each solution is called an individual. Here, an individual is composed of a series of width-depth ratios.

[0153] b. Evaluation of fitness: define a fitness function to measure the quality of each individual. The higher the value of the fitness function, the better the individual.

[0154] c. Selection: select individuals for reproduction from the current population, and the selection probability is proportional to the fitness of the individual.

[0155] d. Crossover (hybridization): two individuals exchange part of the gene to produce new offspring.

[0156] e. Mutation: change a certain gene of an individual with a certain probability to increase the diversity of the population.

[0157] f. Iteration: repeat the above process until the preset number of iterations is reached or other termination conditions are met.

[0158] Objective function:

[0159] Minimum energy loss:

[0160]

[0161] In the formula, f is the objective function, h w is the energy loss;

[0162] Constraint condition:

[0163] Width-depth ratio range constraint:

[0164] λ-2≤λ≤λ+2 (3-19)

[0165] where λ is the width-depth ratio.

[0166] Water flow continuity constraint:

[0167] Q = Av (3-20)

[0168] Topographic factor constraint:

[0169]

[0170] where S is the topographic constraint factor, that is, the topographic constraint factor of the adjacent section.

[0171] 5. Constructing the underwater topography of the river channel based on the water surface width sequence of the river channel flow section and the target width-depth ratio sequence of the river channel flow section;

[0172] Based on the water surface width sequence of the river channel flow section and the target width-depth ratio sequence of the river channel flow section, the water depth and river bottom elevation of all flow sections in the river channel flow section are determined; based on the section shape of the flow section, the river bottom elevation is interpolated to construct the underwater topography of the river channel.

[0173] Specifically as follows:

[0174] Using the target width-depth ratio sequence of the river channel flow section, combining the river water surface vector based on optical remote sensing image, and determining the water surface width sequence of the river channel flow section, the water depth h of all flow sections in the river channel flow section can be further calculated i and the river bottom elevation H 河底 .

[0175] Flow section calculation of river bottom elevation

[0176] H 河底 = Z i -h i

[0177] λ is the width-depth ratio; Q is the flow of the flow section; λ is the width-depth ratio; S is the hydraulic slope; Z i , Z i+1 is the water level of the adjacent two sections; L is the length of the river center line between the two sections, in units of m.

[0178] Using geographic information system tools, along the river flow direction, taking each flow section in the river channel flow section as a section perpendicular to the river flow direction, drawing the profile of the corresponding river bottom elevation and water surface elevation of each flow section, and interpolating the river bottom elevation according to the determined flow section shape to construct the underwater topography of the river channel.

[0179] 6, the underwater topography of the river channel and the elevation model data of the river channel are fused, and the reconstruction of the river channel topography is completed.

[0180] The underwater topography of the river channel and the elevation model (DEM) data of the river channel are fused, and the reconstruction of the river channel topography is completed.

[0181] Step1: Cross-section river bottom elevation interpolation processing

[0182] Let x i be the cross-section position coordinate along the river flow direction (i=1, 2, …, n), y i be the river bottom elevation of the corresponding cross-section. The cubic spline interpolation formula is used:

[0183]

[0184] Where a i , b i , c i , d i are undetermined coefficients, which need to be determined according to the following conditions:

[0185] S(x i ) = y i (interpolation condition, that is, the value of the spline function at each cross-section position is equal to the river bottom elevation of the cross-section).

[0186] S'(x i+1 ) = S'(x i )(first derivative continuity condition).

[0187] S''(x i+1 ) = S''(x i )(second derivative continuity condition).

[0188] Use the "interpolation analysis" tool in the geographic information system tool to select the spline interpolation method to interpolate the river bottom elevation. In the "interpolation analysis" dialog box, set the interpolation data source to the calculated river bottom elevation data, and set appropriate interpolation parameters, such as interpolation method, search radius, etc. After interpolation, continuous underwater topography data will be obtained.

[0189] Step2: Fusion with original DEM grid data

[0190] The interpolated underwater terrain data is fused with the original DEM grid data using the "raster calculator" tool of the geographic information system tool. In the "raster calculator" dialog box, the expressions of the original DEM grid data and the interpolated underwater terrain data are set, the weighted average method is adopted, and the appropriate weight coefficient is set to obtain the fused DEM grid file containing the underwater terrain. That is, the reconstruction of the river channel terrain is completed.

[0191] 7. Collect optical remote sensing images of different periods of the river channel, perform steps 2-6 multiple times to reconstruct the river channel terrain, and optimize the accuracy of the river channel terrain.

[0192] Collect optical remote sensing images of different periods from dry season to flood season, perform steps 2-6 multiple times to reconstruct the river channel terrain, define the water level in the dry season as the low water level, and define the water level in the flood season as the high water level.

[0193] During the multiple river channel terrain reconstruction process, for the same flow section in the river channel terrain, the low water depth of the flow section and the high water depth of the flow section are analyzed for error. Error evaluation indicators can be used for analysis, including: mean square error (MSE), mean absolute error (MAE) or relative error error indicators for quantitative analysis, and specific analysis of the difference value of the section water depth. The following are the calculation formulas of these indicators, which are applicable to the analysis of the accuracy of the reconstructed section water depth under different water level conditions. Mean square error (MSE): This index measures the square average of the difference between the low water level and the high water level, and is used to quantitatively evaluate the overall error of the water depth difference. The index can be expressed as:

[0194]

[0195] Where n is the total number of flow section samples; is the water depth of the i-th section at the low water level; is the water depth of the i-th section at the high water level.

[0196] Mean absolute error (MAE): This index calculates the absolute value of the difference between the low water level and the high water level, providing a more intuitive error measurement, and is less affected by outliers:

[0197]

[0198] Relative error (RE): The relative error is the proportion of the difference between the low water level and the high water level to the high water level, expressed in percentage form, and used to compare the relative degree of difference between different sections:

[0199]

[0200] In the iteration process of the genetic algorithm, the parameters such as the constraint range of the width-depth ratio are continuously adjusted according to error analysis, so as to enhance the convergence speed and accuracy of the algorithm under specific conditions. Specifically, when the error is large, the current width-depth ratio value is not accurate enough, the search range of the width-depth ratio can be expanded, so that the algorithm can explore in a larger solution space, and the possibility of finding the optimal solution is increased; when the error is small, it indicates that the width-depth ratio is close to the optimal solution, and the search range can be appropriately reduced, so as to reduce the invalid search area and accelerate the convergence speed of the algorithm. Through the above multiple reconstruction of the river terrain and error analysis and parameter adjustment in the reconstruction process, the high-precision river terrain is finally obtained.

[0201] The technical scheme in the embodiment of the present application has at least the following technical effects or advantages:

[0202] The technical scheme of the present application calculates the hydrological parameters corresponding to the section of the river terrain based on the measured data of the hydrological station, that is, the geometric shape of the section and the corresponding power function model are determined through generalization analysis of the section of the river bottom, the hydrological parameters corresponding to the section of the river terrain are determined according to the area formula of the section shape combined with the shape coefficient in the power function model. The target width-depth ratio of the river flow section is determined based on the hydraulic parameters and the energy loss of the river flow section, and the river bottom elevation is determined in combination with the water surface width extracted from the optical remote sensing image. The underwater terrain of the river bottom is constructed according to the shape of the flow section and the river bottom elevation, and the reconstruction of the river terrain is completed. The scheme of the present application takes the measured data and optical remote sensing image of the river as the data basis, considers the characteristics of multiple water conservancy parameters corresponding to the flow section and the energy loss, and can reconstruct a more accurate river terrain.

[0203] Figure 6 The device for reconstructing river terrain based on energy loss and genetic algorithm coupling provided in the embodiment of the present application is shown in the figure, and the device comprises:

[0204] The collecting module is used for collecting the optical remote sensing image of the river, the measured data of the hydrological station near the river, and the elevation model data of the river.

[0205] The determining module is used for determining the water surface width sequence of the river flow section based on the river water surface vector of the optical remote sensing image.

[0206] The analyzing module is further used for analyzing the measured data of the hydrological station near the river, and determining the hydraulic parameters corresponding to the river flow section.

[0207] The calculating module is used for determining the target width-depth ratio sequence of the river flow section based on the hydraulic parameters corresponding to the river flow section, taking the minimum energy loss as the optimization target, and adopting the genetic algorithm.

[0208] The constructing module is configured to construct the underwater topography of the river channel based on the water surface width sequence of the river channel flow cross section and the target width-depth ratio sequence of the river channel flow cross section.

[0209] The fusing module is configured to fuse the underwater topography of the river channel with the elevation model data of the river channel to complete the reconstruction of the river channel topography.

[0210] It should be noted that, for the convenience of description, Figure 6 Exemplarily, only the main modules of the river channel topography reconstruction device structure based on the coupling of energy loss and genetic algorithm are shown. In actual application, the system can also include modules or components not shown in the figure; the system is not limited to the above module structure, and can also be other module structures for implementing the above method embodiments.

[0211] Figure 7 A structural schematic diagram of an electronic device provided in an embodiment of the present application is shown in the figure. The electronic device includes a processor and a memory.

[0212] The processor is configured to read and execute the programs and instructions stored in the memory, so that the electronic device executes the above method embodiments.

[0213] It should be noted that, for the convenience of description, Figure 7 Exemplarily, only the main components of the electronic device are shown. In actual application, the electronic device can also include components or components not shown in the figure.

[0214] The present application also provides a computer readable storage medium storing programs or instructions, which, when read and executed by a computer, enable the computer to execute the above method embodiments.

[0215] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; 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 application.

Claims

1. A river terrain reconstruction method based on energy loss and genetic algorithm coupling, characterized in that: include: Collect optical remote sensing images of the river, measured data from hydrological stations near the river, and elevation model data of the river; Determine the water surface width sequence of the river flow section based on the river surface vector of the optical remote sensing image; Analyze the measured data of the hydrological stations near the river to determine the hydraulic parameters corresponding to the flow section of the river; Based on the hydraulic parameters corresponding to the river flow section and taking the minimum energy loss as the optimization goal, a genetic algorithm is used to determine the target width-to-depth ratio sequence of the river flow section. Construct the underwater topography of the river channel based on the water surface width sequence of the river channel flow section and the target width-to-depth ratio sequence of the river channel flow section; The underwater topography of the river is integrated with the elevation model data of the river to complete the reconstruction of the river topography; The analysis of measured data from hydrological stations near the river channel to determine the hydraulic parameters corresponding to the flow section of the river channel includes: Conduct generalized analysis on the measured cross-section data from the hydrological station to determine the fitting model of the river flow cross-section morphology; The river flow section is fitted by fitting the morphological coefficient of the model, and the shape of the river flow section is generalized into a geometric shape; Determine the hydraulic radius of the river flow section based on the area formula of the river flow section with geometric shape; Determine the flow velocity of the river flow section based on the hydraulic radius of the river flow section and Manning's formula; The method of determining the target width-to-depth ratio sequence of the river flow section using a genetic algorithm based on the hydraulic parameters corresponding to the river flow section and taking minimum energy loss as the optimization goal includes: Based on the hydraulic parameters of adjacent flow sections in the river flow section, the Darcy-Weisbach algorithm is used to determine the energy loss of adjacent flow sections. Based on the energy loss of adjacent flow sections, the Bernoulli equation is used to determine the theoretical water level of adjacent flow sections. Obtain the initial width-to-depth ratio sequence of the river flow section; Based on the initial width-to-depth ratio, terrain factor constraints, width-to-depth ratio range constraints, and water flow continuity constraints, and with the minimum energy loss as the optimization goal, a genetic algorithm is used to determine the target width-to-depth ratio sequence of the flow section of the river crossing.

2. The river terrain reconstruction method based on energy loss and genetic algorithm coupling according to claim 1 is characterized in that: The constructing of the underwater topography of the river channel based on the water surface width sequence of the river channel flow section and the target width-to-depth ratio sequence of the river channel flow section includes: Based on the water surface width sequence of the river flow section and the target width-to-depth ratio sequence of the river flow section, the water depth and riverbed elevation of all the flow sections in the river flow section are determined; Based on the cross-sectional morphology of the flow section, the riverbed elevation is interpolated to construct the underwater topography of the river.

3. A river terrain reconstruction device based on energy loss and genetic algorithm coupling, characterized in that: include: The collection module is used to collect optical remote sensing images of the river, measured data from hydrological stations near the river, and elevation model data of the river; A determination module is used to determine the water surface width sequence of the river flow section based on the river surface vector of the optical remote sensing image; The analysis module is used to analyze the measured data of the hydrological stations near the river channel and determine the hydraulic parameters corresponding to the flow section of the river channel; A calculation module is used to determine the target width-to-depth ratio sequence of the river flow section based on the hydraulic parameters corresponding to the river flow section and with the minimum energy loss as the optimization goal; A construction module is used to construct the underwater topography of the river channel based on the water surface width sequence of the river channel flow section and the target width-to-depth ratio sequence of the river channel flow section; The fusion module is used to fuse the underwater topography of the river with the elevation model data of the river to complete the reconstruction of the river topography; The analysis of measured data from hydrological stations near the river channel to determine the hydraulic parameters corresponding to the flow section of the river channel includes: Conduct generalized analysis on the measured cross-section data from the hydrological station to determine the fitting model of the river flow cross-section morphology; The river flow section is fitted by fitting the morphological coefficient of the model, and the shape of the river flow section is generalized into a geometric shape; Determine the hydraulic radius of the river flow section based on the area formula of the river flow section with geometric shape; Determine the flow velocity of the river flow section based on the hydraulic radius of the river flow section and Manning's formula; The method of determining the target width-to-depth ratio sequence of the river flow section using a genetic algorithm based on the hydraulic parameters corresponding to the river flow section and taking minimum energy loss as the optimization goal includes: Based on the hydraulic parameters of adjacent flow sections in the river flow section, the Darcy-Weisbach algorithm is used to determine the energy loss of adjacent flow sections. Based on the energy loss of adjacent flow sections, the Bernoulli equation is used to determine the theoretical water level of adjacent flow sections. Obtain the initial width-to-depth ratio sequence of the river flow section; Based on the initial width-to-depth ratio, terrain factor constraints, width-to-depth ratio range constraints, and water flow continuity constraints, and with the minimum energy loss as the optimization goal, a genetic algorithm is used to determine the target width-to-depth ratio sequence of the flow section of the river crossing.

4. The river terrain reconstruction device based on energy loss and genetic algorithm coupling according to claim 3 is characterized in that: The building block is further used to: Based on the water surface width sequence of the river flow section and the target width-to-depth ratio sequence of the river flow section, the water depth and riverbed elevation of all the flow sections in the river flow section are determined; Based on the cross-sectional morphology of the flow section, the riverbed elevation is interpolated to construct the underwater topography of the river.

5. An electronic device, characterized in that: include: processor and memory; The processor is coupled to a memory; The processor is configured to read and execute the program or instruction stored in the memory, so that the device executes the method according to any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that A computer program is stored, and when the program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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