Sandy coast stability-oriented multi-factor coast system repair optimization method

Abstract

The invention relates to the technical field of coast engineering analysis, in particular to a sandy coast stability-oriented multi-factor coast system restoration optimization method, which comprises the following steps of: identifying a to-be-restored section which faces a serious erosion or degradation problem in a target sandy coast; setting a repairing target; constructing a multi-factor restoration scheme set comprising engineering measure factors and ecological measure factors; based on simulation of a preset hydrodynamic force-sediment transportation coupling model, an evaluation index of each restoration scheme simulation result is extracted; comparing the evaluation index of each repair scheme with a repair target, and calculating a comprehensive benefit value of each repair scheme; selecting the restoration scheme with the highest comprehensive benefit value as an optimal restoration scheme; and outputting an optimal repairing scheme. According to the method, each restoration scheme is converted into a physical input scene in the model by establishing the hydrodynamic force-sediment transportation coupling model, and the dynamic response of the restoration scheme to shoreline change and vegetation survival is simulated and predicted.

Description

Technical Field

[0001] This invention relates to the field of coastal engineering analysis technology, and in particular to a multi-factor coastal system restoration and optimization method for the stability of sandy coastlines. Background Technology

[0002] Sandy coastlines are a common coastal landform type in coastal areas. Their coastline morphology and topographic structure are subject to long-term natural dynamics such as wind, waves, currents, and sediment transport, exhibiting highly dynamic characteristics. Traditional coastal restoration methods mainly rely on engineering measures, such as the construction of hard structures like offshore breakwaters, groynes, and submerged breakwaters, attempting to control coastline retreat by altering wave propagation paths and coastal current structures.

[0003] However, these methods suffer from limitations due to their singular intervention approach, making them ill-suited to complex dynamic evolution processes. Erosion mechanisms on sandy coasts are typically driven by a combination of factors, and single structural measures often have limited effectiveness under complex wave and current conditions, potentially even triggering new sediment disturbances or erosion transfer to adjacent shorelines. In their natural state, coastal vegetation plays a crucial role in regulating shoreline evolution through mechanisms such as root-based sand fixation, flow obstruction and energy dissipation, and accelerated sedimentation. However, traditional engineering restoration schemes often fail to incorporate vegetation systems into their design, resulting in a disconnect between ecological restoration and physical stability objectives, thus reducing overall restoration effectiveness. Existing restoration projects rely on analogical judgments for parameter selection, lacking simulation verification and quantitative optimization analysis, which can easily lead to schemes deviating from actual geomorphological response patterns, resulting in resource waste or suboptimal results.

[0004] Furthermore, while existing hydrodynamic and shoreline evolution model studies can simulate a fixed structural configuration, they generally lack mechanisms for generating and optimizing batch solutions for combinations of multiple measures, and have not established a collaborative parameterized modeling method between engineering and ecological factors. This makes it difficult for existing technologies to support restoration and optimization processes for practical engineering decisions. Summary of the Invention

[0005] This invention provides a multi-factor coastal system restoration optimization method for sandy coast stability. It integrates engineering and ecological measures, uses a high-fidelity coupling model to quantitatively simulate multi-factor schemes, and combines set objectives to conduct comprehensive benefit evaluation and intelligent selection. This method aims to improve the scientific nature, controllability, and ecological adaptability of restoration decisions, and meet the practical needs of modern integrated coastal zone management.

[0006] A multi-factor coastal system restoration optimization method for sandy coast stability includes the following steps: S1: Based on historical topographic data and remote sensing imagery, identify sections of the target sandy coastline facing severe erosion or degradation that require restoration; and set restoration targets. S2: For the section to be restored, construct a multi-factor restoration scheme set including engineering measures and ecological measures; the engineering measures include the spacing and length of the offshore dikes; the ecological measures include the coverage rate of vegetation-promoting plants. S3: Based on the preset hydrodynamic-sediment transport coupling model, simulate each restoration scheme in the multi-factor restoration scheme set, and extract the evaluation index of the simulation results of each restoration scheme, including shoreline change rate and vegetation survival rate. S4: Compare the evaluation indicators of each restoration plan with the restoration objectives, and calculate the comprehensive benefit value of each restoration plan; the comprehensive benefit value is the weighted sum of the normalized shoreline change rate and the vegetation survival rate; S5: Compare the comprehensive benefit values ​​of all repair schemes, select the repair scheme with the highest comprehensive benefit value as the optimal repair scheme, and output the optimal repair scheme.

[0007] Optionally, step S1 acquires a historical remote sensing image sequence and historical topographic monitoring data of the target sandy coastline for at least ten years; interprets the historical remote sensing image sequence, extracts the historical coastline location, calculates the long-term linear change rate of each coastline segment, and initially identifies segments with a long-term linear change rate less than a preset erosion threshold as erosion segments; based on the historical topographic monitoring data, calculates the changes in coastal elevation and volume of the erosion segments, and finally determines segments with a volume change rate exceeding a preset degradation threshold as segments to be restored.

[0008] Optionally, the repair target is set based on the degree of erosion and degradation of the section to be repaired, and specifically includes: Shoreline stability target: It is required that during the simulation period after the implementation of the plan, the rate of change of the shoreline changes from negative to positive or at least reaches a stable state. Vegetation restoration target: The survival rate of the growth-promoting plants should not be lower than the preset percentage at the end of the simulation period.

[0009] Optionally, S2 further includes determining the level values ​​of multiple factors; The horizontal values ​​of the spacing between the offshore breakwaters are set as a first spacing, a second spacing, and a third spacing, wherein the first spacing is smaller than the second spacing, and the second spacing is smaller than the third spacing; The horizontal values ​​of the offshore breakwater length are set as a first length, a second length, and a third length, wherein the first length is less than the second length, and the second length is less than the third length; The planting coverage rate of the growth-promoting plants is set as a first coverage rate, a second coverage rate, and a third coverage rate, wherein the first coverage rate is less than the second coverage rate, and the second coverage rate is less than the third coverage rate.

[0010] Optionally, S2 employs an experimental design method to combine different levels of the three factors—the spacing of the offshore dikes, the length of the offshore dikes, and the coverage rate of the vegetation-promoting plants—to generate a multi-factor restoration scheme set that includes several restoration schemes.

[0011] Optionally, S3 includes parameterizing the engineering measures factors of each restoration scheme in the multi-factor restoration scheme set as boundary conditions and topographic constraints of the hydrodynamic-sediment transport coupling model, wherein the spacing and length of the offshore dikes are used to define the location and geometry of the offshore dikes in the computational grid; and parameterizing the ecological measures factors as input parameters of the ecological module of the model, wherein the vegetation coverage rate is used to calculate the vegetation roughness coefficient and root sand fixation strength in the model grid cell.

[0012] Optionally, the hydrodynamic-sediment transport coupled model can be run to simulate the wave field, sediment transport, and coastal morphology evolution under the combined effects of the engineering and ecological measures within a preset time period.

[0013] Optionally, after the hydrodynamic-sediment transport coupled model has completed its simulation, the simulation results are extracted, and evaluation indicators including shoreline change rate and vegetation survival rate are calculated, wherein: The rate of shoreline change is calculated by comparing the elevation data at the beginning and end of the simulation to determine the average level of shoreline movement in the section to be repaired. The vegetation survival rate is calculated based on the wave dynamics and sedimentary environment at the end of the simulation period. According to the preset critical conditions for plant survival, the percentage of surviving vegetation area to the initial planting area is calculated.

[0014] Optionally, S4 includes comparing the simulation results of each restoration scheme with the restoration target set in S1 to determine whether it meets the minimum target requirements; for restoration schemes that meet the minimum target requirements, extracting the simulated values ​​of shoreline change rate and vegetation survival rate respectively, and using the extreme value normalization method to normalize the simulated values ​​of shoreline change rate and vegetation survival rate to the [0,1] interval respectively; assigning weight coefficients to shoreline change rate and vegetation survival rate according to the priority of restoration targets, and then calculating the comprehensive benefit value of each restoration scheme.

[0015] Optionally, the optimal restoration scheme includes recommended values ​​for the spacing of the offshore breakwaters, the length of the offshore breakwaters, and the coverage rate of vegetation planting, wherein: The recommended value for the spacing of the offshore breakwaters is the specific value of the spacing of the offshore breakwaters used in the optimal repair scheme. The recommended value for the length of the offshore breakwater is the specific value of the offshore breakwater length used in the optimal repair scheme. The recommended value for the plant coverage rate of growth-promoting plants is the specific value of the plant coverage rate of growth-promoting plants used in the optimal remediation scheme.

[0016] The beneficial effects of this invention are: This invention proposes a restoration optimization method that integrates engineering measures (offshore dikes) and ecological measures (planting vegetation to promote growth). It is not limited to single structural methods or traditional dike reinforcement, but rather utilizes three-factor coupled modeling and scheme generation to consider multiple factors such as hydrodynamic regulation, sediment transport, and ecological function reconstruction, reflecting a holistic understanding of the "coastal system." In particular, by converting vegetation cover into roughness factors and sand-fixing parameters in the model, and quantitatively embedding ecological functions into the coastal dynamic evolution model, it truly achieves integrated ecological-engineering optimization design, enhancing the stability, sustainability, and resilience of the restoration.

[0017] This invention establishes a hydrodynamic-sediment transport coupled model, transforming each restoration scheme into a physical input scenario within the model. It simulates and predicts the dynamic response of each scheme to shoreline changes and vegetation survival, moving away from empirical rules or single-index judgments. It proposes a method for calculating plant survival rates based on wave-current shear force and sediment thickness conditions, making ecological effectiveness no longer a subjective assumption but a calculable and quantifiable output indicator of the model. Simultaneously, a numerical comparison mechanism for shoreline change rates is introduced, calculating shoreline retreat / advance rates by simulating the difference in topography before and after restoration, accurately assessing the controllability of each scheme on shoreline evolution trends.

[0018] This invention constructs a goal-oriented benefit evaluation and scheme selection mechanism. Based on shoreline stability and vegetation restoration as dual objectives, it proposes a two-layer screening mechanism based on whether the objectives are met and the level of benefit. Unqualified schemes are eliminated by setting target thresholds to ensure the engineering feasibility and ecological baseline of the schemes. Then, the evaluation indicators are standardized using the extreme value normalization method, and weight coefficients are introduced to reflect the importance of different objectives. Finally, a weighted summation model is used to calculate the comprehensive benefit value, evaluate the merits of each scheme, automatically select the optimal scheme, and output its corresponding three recommended parameter values. This invention integrates the advantages of simulation modeling, indicator standardization, and multi-objective optimization, forming a complete technical closed loop from objective setting to scheme output. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the optimization method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the calculation process of the comprehensive benefit value in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. For some well-known technologies, those skilled in the art may also use other alternative methods to implement the invention. Moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0022] like Figures 1-2 As shown, a multi-factor coastal system restoration optimization method for sandy coast stability includes the following steps: S1: Based on historical topographic data and remote sensing imagery, identify sections of the target sandy coastline that are facing severe erosion or degradation and set restoration targets.

[0023] S11, Historical Data Acquisition: Acquire historical remote sensing image sequences and historical topographic monitoring data for at least ten years for the target sandy coastline. The remote sensing images may include satellite images, aerial images, or UAV images, and the topographic monitoring data may include RTK measured data, lidar point cloud data, or three-dimensional topographic reconstruction results.

[0024] S12, Preliminary identification of erosion zone: The historical remote sensing image sequence was interpreted over time to extract the shoreline location for each year, and the long-term shoreline change rate for each segment was calculated using linear regression analysis. The calculation formula is as follows: ;in, This indicates the rate of change of the shoreline (unit: m / year). This represents the cumulative change in the position of the shoreline during the observation period. This indicates the corresponding time span; sections of shoreline meeting the following conditions are identified as initial erosion zones: ;in, This indicates the preset shoreline erosion threshold. The target value is -0.5m / year. A shoreline retreat rate exceeding 0.5m / year will pose a significant threat to nearshore ecology and infrastructure, requiring priority intervention.

[0025] The purpose of S12 is to initially identify sections of coastline that have been experiencing significant long-term retreat within a large target sandy coastline, providing a basis for subsequent more refined topographic analysis and restoration target setting. Using acquired historical remote sensing image sequences, the coastline position at each moment is extracted through temporal interpretation (edge ​​detection). These coastline position data are constructed chronologically to form a coastline temporal sequence, with each coastline corresponding to a specific time point, clearly reflecting the evolution trajectory of the coastline position of a particular coastal segment over the past several years. For each coastline segment, based on its annual coastline position changes, linear regression analysis is applied to fit its trend and calculate its long-term coastline change rate, i.e., the average distance the coastline moves per unit time.

[0026] The physical meaning of this rate is: If the result is negative, it indicates that the coastline is retreating towards the land, i.e., erosion has occurred; If the result is positive, it indicates that the coastline extends into the sea, resulting in siltation or topographic growth.

[0027] The use of linear regression can eliminate the interference of short-term fluctuations on judgment; it quantifies the strength of the trend and provides a standard for subsequent threshold judgment.

[0028] Coastline temporal interpretation: This involves extracting the coastline location at each moment through edge detection, primarily based on the abrupt changes in grayscale or spectral features between land and water in remote sensing images. First, the remote sensing image is preprocessed (denoising and enhancement). Then, an edge detection algorithm is applied to identify the locations with the most significant changes in brightness gradients. Due to the significant differences in spectral reflectance between seawater and land, their boundary (i.e., the coastline) typically appears as a clearly defined linear boundary in the image. By extracting the pixel coordinates of this linear boundary and combining them with the image's georegistration information, the coastline location can be converted into true geographic coordinates for subsequent time series analysis and rate of change calculation.

[0029] S13, Precise Confirmation of Degraded Sections: After the initial identification of eroded sections, further in-depth analysis is conducted on these initially selected eroded sections based on high-precision topographic monitoring data. This aims to identify the sections with the most severe topographic degradation and those urgently requiring restoration. The main objective is to determine whether there is significant sand loss, depression, or other degradation trends in the area by quantitatively calculating the volume change rate of the coastal topography. Details are as follows: Based on the historical topographic monitoring data, the cross-sectional elevation changes and three-dimensional topographic volume changes of the initial erosion sections were analyzed, and the volume change rate of each coastal segment was calculated. Its expression is: ;in, This represents the rate of volume change (unit: m³ / m² / year, indicating the volume change per square meter per year). , These represent the initial time. Washin-jitsu The topographic volume is given by A, which represents the planar projected area of ​​the corresponding shoreline segment. The segments that meet the following conditions are identified as the final segments to be restored: ;in, The preset topographic degradation threshold is set at -0.1 m³ / m² / year. Numerous field studies on coastal evolution have shown that under natural erosion, the average annual topographic volume change rate of uninterrupted sandy coastlines is approximately -0.05 to -0.08 m³ / m² / year. When the volume change rate exceeds -0.1 m³ / m² / year, it is accompanied by severe degradation phenomena such as significant topographic depression, wind erosion pit development, or tidal creek intrusion.

[0030] S14, Setting Restoration Objectives: After identifying the sections to be restored, two quantifiable, comparable, and verifiable restoration objectives are proposed to evaluate and select subsequent restoration schemes. These objectives correspond to the two core restoration directions for sandy coastlines and will serve as the basic criteria for evaluating the merits of various subsequent restoration schemes. Specifically: Based on the historical erosion and degradation rates of each section to be restored, the following two quantifiable restoration objectives are set: Shoreline stability target: The rate of shoreline change of the proposed restoration scheme at the end of the simulation period. It should meet the following requirements: This objective focuses on whether the evolution trend of the coastline can be reversed or controlled. Specifically, if a coastline is in a state of long-term erosion, that is, the coastline retreats inland year by year, then effective restoration should at least stop its retreat, and ideally, convert it into siltation; therefore, the objective is set as follows: the rate of coastline change during the simulation period after restoration should not be less than 0 meters / year; this means: The speed is 0, meaning the shoreline is basically stable; A positive velocity indicates that the shoreline is shifting forward and there is slight siltation, which is a positive effect. If the value is still negative, it means that the repair measures are ineffective and do not meet the objectives; This objective ensures the stability of the basic topography, which is the most crucial engineering goal. Vegetation restoration target: The survival rate of the growth-promoting plants in the described restoration scheme at the end of the simulation period. It should meet the following requirements: This objective focuses on the restoration of ecological functions after restoration, especially when ecological restoration methods are employed. Planting growth-promoting plants during the simulation period is merely a means; the survival rate is the key to measuring the effectiveness of ecological restoration. Therefore, the target is set as follows: at the end of the simulation period, the survival rate of plants should not be lower than a certain preset minimum value of 70%.

[0031] in, This represents the simulated rate of change of the shoreline. This represents the simulated vegetation survival rate. This indicates the preset minimum acceptable vegetation survival rate (70%).

[0032] S2: For the section to be restored, construct a multi-factor restoration scheme set including engineering measures and ecological measures; the engineering measures include the spacing and length of the offshore dikes; the ecological measures include the coverage rate of vegetation-promoting plants.

[0033] In this invention, to construct a well-structured and comprehensive "multi-factor restoration scheme set," specific selectable value ranges need to be set for each control factor participating in the scheme combination; that is, multiple "level values" are set for each factor. This concretizes and engineers the abstract restoration parameters, providing clear input variables for subsequent model simulations and ensuring the systematicity and comparability of the experimental design. Three key factors were selected: the spacing of the offshore breakwaters, the length of the offshore breakwaters, and the coverage rate of vegetation planting, covering typical representatives of hydrodynamic structural measures and ecological restoration measures. Each factor was assigned three representative level values, set at low, medium, and high intensities, as follows: 1. Spacing between offshore breakwaters: This factor controls the horizontal distance between two adjacent offshore breakwaters, i.e. the density of the breakwater layout; the smaller the value, the denser the breakwaters, the stronger the wave blocking effect, but it may also bring greater costs and ecological disturbance; the larger the value, the larger the structural gaps, the weaker the impact on shoreline stability, but the lower the degree of ecological intervention.

[0034] 2. Offshore breakwater length: This factor determines the vertical distance of each offshore breakwater along the coastline, i.e., the scale and control range of the structure itself. The longer the breakwater, the larger the coverage area and the stronger the impact on hydrodynamics and sediment transport, but the higher the engineering cost. Setting multiple length levels helps to assess the specific contribution of the structure's scale to stability and ecological effects.

[0035] 3. Coverage of growth-promoting plants: This factor reflects the intensity of ecological restoration measures, that is, the proportion of growth-promoting plants planted per unit area. The higher the coverage, the stronger the effect of vegetation on sand fixation, tidal energy absorption and habitat restoration, but the planting and subsequent maintenance costs also increase accordingly. Through multi-level settings, the marginal benefits of coverage on vegetation survival rate and overall restoration effect can be analyzed.

[0036] S2 specifically includes: S21 sets multiple level values ​​for the following three factors for engineering and ecological measures intended for sandy coastline restoration, as detailed below: S211. The horizontal value of the spacing D between offshore breakwaters is set as follows: ;in, These represent different deployment densities, which can be set to 50m, 100m, and 150m; S212. The horizontal value of the offshore breakwater length L is set as follows: ;in, These represent different structural sizes, and can be set to 80m, 100m, and 120m.

[0037] S213. The level value of the coverage rate C for growth-promoting plants is set as follows: ;in, These represent different ecological restoration intensities, which can be set to 30%, 50%, and 70%.

[0038] S22, based on the above three factors and their respective three levels, uses experimental design methods to combine different level values ​​to form a multi-factor repair scheme set S containing multiple possible parameter configurations: ;in, The theory will generate A combination scheme, each triplet This refers to a specific restoration plan, which includes the corresponding engineering and ecological parameter settings.

[0039] The experimental design method employed is a typical full factorial design, which enumerates all possible values ​​for all factors (variables) participating in the experiment, referred to as level values. Then, all possible combinations of these factors are permuted, generating a set of experimental schemes covering the entire design space. This ensures that no possible factor combination is overlooked, and allows for a systematic analysis of the individual effects (main effects) and combined effects (interaction effects) of each factor. The final selected "optimal combination" possesses generalized global optimality characteristics.

[0040] In this invention, there are three control factors, and each factor has three level values, as shown in Table 1 below.

[0041] Table 1. Example of Factor-Level Values

[0042] Using a full factorial design, that is, combining the three values ​​of each factor with all values ​​of other factors, theoretically 27 repair schemes can be formed, each repair scheme corresponding to a triplet combination form: That is: Option 1 is ( , , Option 2 is () , , ), ..., until scheme 27 is ( , , The resulting scheme set includes low-input, low-intensity combinations (small spacing, short dikes, low coverage), as well as high-intensity, high-cost combinations (long dikes, high-density vegetation), and also covers various medium-sized configurations or mixed combinations, which is conducive to a comprehensive assessment of the specific impacts of different strategies on coastal stability and ecological restoration.

[0043] For example: Suppose the following specific values ​​are set: D: Spacing between offshore breakwaters: =50m, =100m, =150m; L: Length of the breakwater =80m, =100m, =120m; C: Vegetation coverage rate: =30%, =50%, =70%; A specific repair solution (e.g., the 14th one) would be: ( , , The formula is (100m, 120m, 30%). This scheme indicates that the distance between the dikes is 100 meters (medium-density layout); the length of each dike section is 120 meters; and the planting coverage rate is 30% (low ecological input). This scheme will be used as an input to the subsequent hydrodynamic-sediment transport coupled model for simulation.

[0044] S3: Based on the preset hydrodynamic-sediment transport coupling model, simulate each restoration scheme in the multi-factor restoration scheme set, and extract the evaluation index of the simulation results of each restoration scheme, including shoreline change rate and vegetation survival rate.

[0045] S31: Parametric modeling of engineering measure factors: For each remediation scheme in the multi-factor remediation scheme set, its engineering measures factors are input into the hydrodynamic-sediment transport coupled model as boundary conditions and topographic constraints of the model, specifically including: The spacing D and length L of the offshore breakwaters are converted into geometric configurations in the model computation grid to define the location, spacing, and dimensions of the offshore breakwaters.

[0046] In the coupled hydrodynamic-sediment transport model, the entire study area is first divided into a two-dimensional or three-dimensional computational grid system, composed of numerous regular or irregular grid cells. Each cell carries key state variables such as velocity, wave height, sediment concentration, and bed elevation. For engineering measures to be incorporated into the simulation, they must be converted into physical boundary conditions or topographic obstacles at the grid level. The task of S31 is to transform the design parameters of the offshore breakwater into a spatial geometry that the model can recognize, in order to accurately simulate its impact on wave propagation, flow path, and sediment transport.

[0047] The geometric configuration of the offshore breakwater in the model mesh is defined as follows: 1. Determine the location of the offshore dike (distance from the shore): Based on the shoreline location, set a "baseline distance from the shore" towards the sea, for example, 100 meters from the shore; the centerline of the dike is laid out parallel to the shoreline, and multiple dikes are arranged in sequence; using this as a reference, select the corresponding grid cell in the model grid as the corresponding position of the dike centerline.

[0048] 2. Define the number and spacing of embankments based on the layout spacing: Set the total length of the repair section as Lcoast and the layout spacing as D. Then the number of embankments is approximately N = Lcoast / D. The starting grid position of each embankment is equal to the position of the previous embankment + D. In the grid coordinate system, mark the center position of each embankment in an equal-interval manner and record its start and end coordinate range.

[0049] 3. Define the coastal-sea dimension of the dike based on its length: Set the total length of each dike to L, which is the extension in the direction perpendicular to the shoreline. Determine the number of cells that the dike spans in the grid longitudinally based on the grid resolution. For each dike, mark it as a dike area or obstacle area on multiple consecutive grid cells at the corresponding location.

[0050] It also includes constructing dike barrier units in the model to affect wave propagation, wave energy dissipation, and coastal flow structure. The dike barrier units in the model are constructed using the topographic elevation method (topographic constraint), which involves setting the bed elevation of the grid unit containing the dike to be much higher than the normal water level. This method can simulate the dike as an insurmountable structure that blocks or reflects waves and flow velocities. At the same time, these elevated units will also act as sediment or scour barriers in the sediment module.

[0051] In this way, the parameters of the engineering measures (offshore breakwater) are embedded into the simulation environment in the form of mesh geometry + model rule parameters, so that each repair scheme becomes a clearly executable physical input in the model.

[0052] S32, Parametric modeling of ecological measures factors: The ecological measure factor, namely the vegetation coverage rate C, is input into the ecological module of the coupled model, and the ecological parameter field is calculated based on this, including: Vegetation roughness coefficient : Represents the resistance of the plant community to fluids; Root system sand fixation strength This indicates the root system's ability to resist disturbance from bed sediments.

[0053] The parameterization process is as follows: 1. In the grid cells of the planted area, the vegetation roughness coefficient is set as follows: ; 2. The root system sand fixation strength is set as follows: ; in, , An empirical mapping function related to coverage C can be set based on plant species, planting density, and experimental data.

[0054] function This is a vegetation roughness coefficient mapping function, which expresses that the higher the vegetation cover, the stronger the fluid resistance, and the greater the bed roughness. Generally, a linear function can be used, expressed in linear form as: ;in, This refers to the surface roughness of the bed when there is no vegetation cover. This is the roughness growth coefficient, representing the increase in roughness per unit coverage.

[0055] function This is a root-based sand-fixing strength mapping function, which expresses that the higher the planting density and the greater the coverage, the stronger the stability of the root system on the subsoil sediment structure. Linear or logarithmic enhancement forms are used: ; Or simplified to: ; in, The disturbance resistance of the bed surface when there are no plants. , is the plant root enhancement effect coefficient, and C is the planting coverage rate (0~1 or 0~100%). This parameter can be fitted by sediment erosion test (or field monitoring data).

[0056] S33, Based on the above input parameters, run the hydrodynamic-sediment transport coupling model to simulate the process for the set simulation duration. Within this context, the restoration measures have a comprehensive impact on the hydrodynamic field, sediment transport process, and shoreline morphology evolution of the target coastal section.

[0057] Key variables output from the simulation process include: wave height field, flow velocity field, bed shear stress distribution, sediment concentration field, and topographic evolution data.

[0058] S34. Based on the model output, the following two core evaluation metrics are extracted: S341, Rate of Change of Shoreline The average rate of shoreline movement in the section to be restored is calculated by comparing the shoreline positions at the beginning and end of the simulation period. ;in, This represents the simulated rate of change of the shoreline. , These represent the positions of the shoreline at the beginning and end of the simulation period, respectively. This is for the simulated duration; S342, Vegetation survival rate Based on the bed shear force of each grid cell at the end of the simulation. and changes in sediment thickness Based on the critical mechanical conditions for plant survival, determine whether plants in each unit can survive, and then calculate the overall survival rate: ;in, For vegetation survival rate, This represents the total area of ​​the vegetation grid that meets the following conditions: and , This represents the initial total planting area. This represents the shear limit of a plant. This represents the maximum erosion depth that the root system can withstand. To simulate the bed shear force at the end of the process, To simulate the variation in deposition thickness during the simulation period, This represents the critical shear strength of the plant.

[0059] S4: Compare the evaluation indicators of each restoration scheme with the restoration target, and calculate the comprehensive benefit value of each restoration scheme; the comprehensive benefit value is the weighted sum of the normalized shoreline change rate and the vegetation survival rate.

[0060] S4 specifically includes the following: S41, Feasibility Screening: For each repair scheme in the multi-factor repair scheme set, extract its simulation results: Rate of change of shoreline ; Vegetation survival rate .

[0061] Compare it with the repair target set in S1: If the following two conditions are met: , If the proposed solution is deemed feasible, it will be included in the subsequent benefit calculation process; otherwise, it will be considered an unqualified solution and will not be included in the comprehensive benefit evaluation.

[0062] For all restoration schemes marked as feasible, S42 extracts the shoreline change rate V and vegetation survival rate S obtained at the end of the simulation period as input values ​​for evaluation indicators.

[0063] S43 uses a normalization method to uniformly transform the indicators into the [0,1] interval, as follows: Normalized value of shoreline change rate : ;in, This represents the maximum rate of shoreline change among all feasible options. The minimum rate of shoreline change among all feasible options. This is the normalized rate of change of the shoreline.

[0064] Normalized value of vegetation survival rate : ;in, Vegetation survival rate in percentage form. The value represents the normalized vegetation survival rate, ranging from [0,1].

[0065] S44, based on the user's or decision-maker's focus on the remediation objectives, set the following weight parameters: Weighting coefficient of shoreline change rate ; Weighting coefficient of vegetation survival rate ; The following constraints must be satisfied: ; This weight setting is used to express decision preferences, such as: When greater emphasis is placed on shoreline stability, it can be set =0.7、 =0.3; When ecological restoration is given greater emphasis, the settings can be reversed.

[0066] S45. Calculation of Comprehensive Benefit Value: For each feasible repair scheme, calculate its comprehensive benefit value E, defined as follows: Where E represents the overall benefit value of the repair plan. , For normalized index values, , These are the weighting coefficients.

[0067] S5: Compare the comprehensive benefit values ​​of all repair schemes, select the repair scheme with the highest comprehensive benefit value as the optimal repair scheme, and output the optimal repair scheme.

[0068] S5 specifically includes sorting all repair schemes that meet the repair objective requirements according to their comprehensive benefit value from high to low, and selecting the repair scheme ranked first as the optimal repair scheme. Based on the selected optimal repair scheme The corresponding control factor parameter values ​​are output as the final recommendation result, specifically including: Recommended values ​​for the spacing of offshore breakwaters: ; Recommended values ​​for the length of the breakwater: ; Recommended coverage values ​​for growth-promoting plants: ; in, , , These represent the spacing, length, and vegetation coverage of the offshore dikes used in the optimal restoration plan, respectively. This is the final recommended value output by this method, which can be used as a reference for practical engineering applications.

[0069] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0070] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-factor coastal system restoration and optimization method for the stability of sandy coastlines, characterized in that, Includes the following steps: S1: Based on historical topographic data and remote sensing imagery, identify the sections of the target sandy coastline that are facing severe erosion or degradation and require restoration. And set repair goals; S2: For the section to be restored, construct a multi-factor restoration scheme set including engineering measures and ecological measures; the engineering measures include the spacing and length of the offshore dikes; the ecological measures include the coverage rate of vegetation-promoting plants. S3: Based on the preset hydrodynamic-sediment transport coupling model, simulate each restoration scheme in the multi-factor restoration scheme set, and extract the evaluation index of the simulation results of each restoration scheme, including shoreline change rate and vegetation survival rate. S4: Compare the evaluation indicators of each restoration plan with the restoration objectives, and calculate the comprehensive benefit value of each restoration plan; the comprehensive benefit value is the weighted sum of the normalized shoreline change rate and the vegetation survival rate; S5: Compare the comprehensive benefit values ​​of all repair schemes, select the repair scheme with the highest comprehensive benefit value as the optimal repair scheme, and output the optimal repair scheme.

2. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 1, characterized in that, S1 acquires a historical remote sensing image sequence and historical topographic monitoring data of the target sandy coastline for at least ten years; interprets the historical remote sensing image sequence, extracts the historical coastline location, calculates the long-term linear change rate of each section of the coastline, and initially identifies sections with a long-term linear change rate less than a preset erosion threshold as erosion sections. Based on the historical topographic monitoring data, the changes in coastal elevation and volume of the eroded section are calculated, and the sections with a volume change rate exceeding a preset degradation threshold are finally identified as sections to be restored.

3. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 2, characterized in that, The repair objectives are set based on the degree of erosion and degradation of the section to be repaired, and specifically include: Shoreline stability target: It is required that during the simulation period after the implementation of the plan, the rate of change of the shoreline changes from negative to positive or at least reaches a stable state. Vegetation restoration target: The survival rate of the growth-promoting plants should not be lower than the preset percentage at the end of the simulation period.

4. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 1, characterized in that, S2 also includes determining the level values ​​of multiple factors; The horizontal values ​​of the spacing between the offshore breakwaters are set as a first spacing, a second spacing, and a third spacing, wherein the first spacing is smaller than the second spacing, and the second spacing is smaller than the third spacing; The horizontal values ​​of the offshore breakwater length are set as a first length, a second length, and a third length, wherein the first length is less than the second length, and the second length is less than the third length; The planting coverage rate of the growth-promoting plants is set as a first coverage rate, a second coverage rate, and a third coverage rate, wherein the first coverage rate is less than the second coverage rate, and the second coverage rate is less than the third coverage rate.

5. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 4, characterized in that, S2 employs an experimental design method to combine different levels of the three factors—the spacing of the offshore dikes, the length of the offshore dikes, and the coverage rate of the vegetation-promoting plants—to generate a multi-factor restoration scheme set that includes several restoration schemes.

6. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 1, characterized in that, S3 includes parameterizing the engineering measures factors of each restoration scheme in the multi-factor restoration scheme set into the boundary conditions and topographic constraints of the hydrodynamic-sediment transport coupling model, wherein the spacing and length of the offshore dikes are used to define the location and geometry of the offshore dikes in the computational grid; and parameterizing the ecological measures factors into the ecological module input parameters of the model, wherein the vegetation coverage rate is used to calculate the vegetation roughness coefficient and root sand fixation strength in the model grid cell.

7. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 6, characterized in that, The hydrodynamic-sediment transport coupled model was run to simulate the wave field, sediment transport and coastal morphology evolution process under the combined effect of the engineering and ecological measures within a preset time period.

8. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 7, characterized in that, After the hydrodynamic-sediment transport coupled model is completed, the simulation results are extracted, and evaluation indicators including shoreline change rate and vegetation survival rate are calculated, among which: The rate of shoreline change is calculated by comparing the elevation data at the beginning and end of the simulation to determine the average level of shoreline movement in the section to be repaired. The vegetation survival rate is calculated based on the wave dynamics and sedimentary environment at the end of the simulation period. According to the preset critical conditions for plant survival, the percentage of surviving vegetation area to the initial planting area is calculated.

9. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 1, characterized in that, S4 includes comparing the simulation results of each restoration scheme with the restoration target set in S1 to determine whether it meets the minimum target requirements; for restoration schemes that meet the minimum target requirements, the simulated values ​​of shoreline change rate and vegetation survival rate are extracted respectively, and the extreme value normalization method is used to normalize the simulated values ​​of shoreline change rate and vegetation survival rate to the [0,1] interval respectively; according to the priority of the restoration target, a weight coefficient is assigned to the shoreline change rate and a weight coefficient is assigned to the vegetation survival rate, and then the comprehensive benefit value of each restoration scheme is calculated.

10. The multi-factor coastal system restoration and optimization method for sandy coast stability according to claim 1, characterized in that, The optimal restoration plan includes recommended values ​​for the spacing of the offshore breakwaters, the length of the offshore breakwaters, and the coverage rate of vegetation planting, wherein: The recommended value for the spacing of the offshore breakwaters is the specific value of the spacing of the offshore breakwaters used in the optimal repair scheme. The recommended value for the length of the offshore breakwater is the specific value of the offshore breakwater length used in the optimal repair scheme. The recommended value for the plant coverage rate of growth-promoting plants is the specific value of the plant coverage rate of growth-promoting plants used in the optimal remediation scheme.

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