An underwater delta erosion and deposition evolution analysis method based on water depth data

Abstract

The application discloses a kind of underwater delta scouring and silting evolution analysis methods based on water depth data, it is related to marine engineering technical field, collects the water depth data of underwater delta cross section of research area in recent years, remote sensing data and hydrological data, and is preprocessed unified coordinate system and elevation datum;Using the measured water depth data after preprocessing, the water depth database of diaokou lobe underwater delta is established, covering each stage of construction period and abandonment.This application integrates multi-temporal remote sensing data and cross section measured water depth data in recent years, constructs the time and space continuous database covering the whole cycle from construction period to abandonment, combined with seven-parameter coordinate conversion and kriging interpolation technology, effectively fills the data missing area, generates continuous water depth surface with spatial resolution ≤50m, breaks through the dynamic monitoring limitations caused by data fault in traditional research, makes scouring and silting evolution analysis from fragmented observation to systematic tracking, and provides data support for accurately evaluating seabed stability.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, specifically to a method for analyzing the erosion and deposition evolution of underwater deltas based on water depth data. Background Technology

[0002] River deltas are located in the transitional zone between land and sea, making them the most active and sensitive areas of land-sea interaction. The Yellow River Delta, as a typical river delta, not only possesses abundant mineral and biological resources but also occupies an important position in terms of ecological environment and natural resources. The scouring and sedimentation changes of the upper and lower deltas directly affect the stability of the nearshore seabed and ecological security. With the rise of coastal economic centers, human activities have had an increasingly significant impact on river deltas. Therefore, studying their scouring and sedimentation evolution is of great significance for protecting the ecological environment and rationally utilizing natural resources.

[0003] In existing technologies, the formation of underwater delta lobes spans nearly 60 years, making it difficult to collect both early and latest measured data. This hinders the systematic reconstruction and dynamic tracking of the scouring and deposition evolution process during the construction and post-abandonment phases. Traditional methods rely on single-source or short-term depth data, which struggles to overcome the analytical limitations caused by inconsistent spatiotemporal resolution. Especially in complex underwater environments with strong tidal and high turbidity interactions, it is difficult to effectively distinguish between false scouring signals caused by short-term turbidity disturbances and the actual seabed instability trend, easily leading to engineering misjudgments. Furthermore, in areas where subsea pipelines or cables are buried, there is a lack of quantitative assessment methods for the cumulative effects of micro-topographical changes, often resulting in delayed or over-designed protective measures. Therefore, this paper proposes an underwater delta scouring and deposition evolution analysis method based on depth data to address the aforementioned problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for analyzing the evolution of underwater delta erosion and deposition based on water depth data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for analyzing the erosion and deposition evolution of underwater deltas based on water depth data includes the following steps: S1. Collect historical cross-sectional measured water depth data, remote sensing data and hydrological data of the underwater delta in the study area, perform preprocessing to unify the coordinate system and elevation benchmark, remove outliers and ensure data consistency. S2. Using the pre-processed measured water depth data, establish a water depth database for the Diaokou Leaf-shaped underwater delta, covering all stages from the construction period to the post-abandonment period. S3. Based on remote sensing data, edge detection is performed to extract historical low tide lines as shoreline benchmarks, and the evolution process of the shoreline of the Diaokouyeban water delta is analyzed to divide the shoreline evolution stages. S4. Combining the water depth database with the shoreline evolution stages, the scouring and deposition evolution stages of the Diaokou Leaf-shaped underwater delta are divided and the evolution characteristics of each scouring and deposition evolution stage are identified. S5. Calculate the scouring and sedimentation volume and scouring rate at each stage of scouring and sedimentation evolution. Use a wavelet transform-hidden Markov model hybrid algorithm to purify the scouring and sedimentation rate data, analyze the scouring and sedimentation evolution trend, and evaluate seabed stability. S6. Based on the purified siltation rate data, construct a cumulative topographic curvature change rate model to identify micro-topographic abrupt change zones and potential pipeline suspension sections, and formulate targeted engineering protection measures.

[0006] A further improvement to the technical solution of the present invention is that: S1 specifically includes: We collected historical cross-sectional measured water depth data, multi-source remote sensing data, and related hydrological data of the underwater delta in the study area. We then performed preliminary classification and organization of the scattered data to ensure that the data covered the complete time series. For all types of data collected, coordinate system transformation is performed according to a unified standard to unify data from different sources to the same coordinate system and elevation datum, eliminating data deviations caused by differences in datums. The same coordinate system is the Gauss-Kruger projection, and the elevation datum is the 1985 National Elevation Datum. Geometric correction is performed on remote sensing data, and water depth data is interpolated to the same grid to ensure spatial alignment. During the processing, the accuracy of projection transformation needs to be checked to avoid coordinate offset. Outliers are detected in the unified data and removed to ensure data quality. Data consistency is checked through cross-validation, and problematic data is corrected to generate a standardized dataset.

[0007] A further improvement to the technical solution of this invention lies in the following: In step S2, the process of establishing the water depth database for the underwater delta of the sloping mouth is as follows: The preprocessed historical cross-sectional measured water depth data were uniformly formatted, including coordinate alignment, unit conversion and metadata annotation. A spatial database architecture was adopted, and data tables were designed to store year, location, water depth value and source information. Spatial indexes were built simultaneously to support efficient querying and ensure data traceability and integrity. The measured water depth data of cross sections over the years were integrated according to time series, classified into construction period and post-abandonment stage, and divided into stage sub-databases. Missing data were filled in by spatiotemporal interpolation, and consistency was checked twice to generate a standardized grid dataset with a resolution of 200m. Establish a database version management system to record the update time, modified content, and responsible person for each update, and design a data interface to support the automatic import and format adaptation of subsequent new measurement data, ensuring the long-term availability of the database.

[0008] A further improvement to the technical solution of the present invention is that: S3 specifically includes: Landsat series remote sensing data were extracted from the preprocessed remote sensing data, and radiometric and geometric corrections were performed to eliminate the effects of atmospheric scattering and topographic distortion. Band combination was used to enhance the contrast of the water-land boundary. The boundary between water and land was identified based on the edge detection algorithm. Historical low tide lines were extracted as shoreline references to ensure spatial consistency. Based on the extracted low tide line data, the shoreline position shift, erosion / siltation rate and curvature changes in different years are calculated to obtain shoreline evolution characteristics including shoreline change rate, spatial displacement direction and shoreline curvature. By combining K-means clustering analysis, multi-temporal shoreline data were extracted to divide the shoreline evolution stages, clarifying the time boundaries and dominant dynamic mechanisms of each shoreline evolution stage. The shoreline evolution stages include the stable period, the erosion period, and the siltation period.

[0009] A further improvement to the technical solution of this invention lies in the following: the process of clarifying the time boundaries and dominant dynamic mechanisms of each shoreline evolution stage is as follows: By integrating multi-temporal shoreline data, shoreline evolution characteristics reflecting the evolutionary state are extracted, including shoreline change rate (EPR), spatial displacement direction (seaward / landward), and shoreline curvature (radius of curvature). Each shoreline evolution characteristic is normalized to eliminate dimensional differences, and dimensionality is reduced by principal component analysis (PCA), retaining principal components with an explanatory power of ≥85%. A feature matrix suitable for cluster analysis is constructed, with rows representing years and columns containing variables such as shoreline change rate, spatial displacement direction, and shoreline curvature. The optimal number of clusters is determined based on the elbow rule and the contour coefficient method, with K=3, corresponding to the stable period, erosion period and siltation period respectively. The K-means algorithm is used to iteratively optimize the feature matrix. The shoreline status is classified by minimizing the sum of squared intra-cluster distances. Combining the clustering results with the time series, the start and end years of each stage are determined, a distribution map of shoreline evolution stages is generated, and the spatiotemporal continuity of the classification boundary is verified. By integrating hydrological, meteorological, and human activity data, and correlating external driving factors such as water and sediment flux, wave energy, and the intensity of human activities, the dominant dynamic mechanisms of each shoreline evolution stage are analyzed. Specifically, the sedimentation stage is associated with high sediment transport and low wave energy, the erosion stage corresponds to the depletion of sediment sources and the action of strong waves, and the stable stage matches artificial shoreline stabilization or natural equilibrium. This ultimately forms a stage-dynamic coupling explanation framework for shoreline evolution.

[0010] A further improvement to the technical solution of the present invention is that: in step S4, the process of identifying the evolutionary characteristics of each stage of scour and sedimentation is as follows: By integrating multi-temporal shoreline data and water depth database, unifying the coordinate system and interpolating missing values, a spatiotemporal continuous dataset covering the Diaokou Leaf-shaped underwater delta is constructed. The shoreline location and water depth profile for each year are extracted, the change in underwater topography elevation is calculated, and the rate of water depth change is determined. Based on the shoreline evolution stages (stable period / erosion period / siltation period) and the rate of water depth change, the underwater delta's scour and sedimentation evolution stages are divided by cluster analysis. The spatial distribution characteristics of each scour and sedimentation evolution stage, including the standard deviation of water depth and the annual rate of slope change, are calculated. The scour and sedimentation evolution stages are divided into the construction period (rapid deposition), the abandonment period (rapid scour), and the adjustment period (alternating scour and sedimentation). By combining spatial distribution characteristics, the evolutionary features of each stage of scour and sedimentation are identified, and the critical threshold for scour and sedimentation transition is determined.

[0011] A further improvement to the technical solution of this invention lies in the following: In step S5, the process of purifying the siltation rate data using a wavelet transform-hidden Markov model hybrid algorithm is as follows: Based on spatiotemporally continuous water depth data in the water depth database, the changes in underwater topographic elevation between adjacent years are calculated pixel by pixel (50m×50m grid). The volume of scour and sedimentation in each stage of scour and sedimentation evolution is quantified by spatial integration. The annual average scour rate is calculated using the sliding window method. Sedimentation and scour areas are distinguished. Combined with the classification results of scour and sedimentation stages, the total scour and sedimentation volume in each stage of scour and sedimentation evolution is statistically analyzed to form a time-series scour and sedimentation rate dataset covering the study area. Wavelet transform is applied to the time-series scouring and silting rate data of each pixel for multi-scale decomposition to extract the fluctuation components at different time scales. Hidden Markov model is used to identify the state of the decomposed signal. By calculating the state transition probability, the true scouring and silting trend is distinguished from the pseudo signal generated by short-term turbidity disturbance, and the purified scouring and silting rate dataset is output.

[0012] A further improvement to the technical solution of this invention is that, in step S5, the process of analyzing the erosion and deposition evolution trend and assessing seabed stability is as follows: Based on the purified scour and sedimentation rate dataset, the scour and sedimentation trend slope is calculated pixel by pixel (50m×50m grid cell) using a linear time series fitting method. The long-term evolution direction is quantified by the least squares method to identify the evolution patterns of accelerated scour, slowed sedimentation and equilibrium. The standard deviation of the scour and sedimentation rate of each pixel during the observation period is extracted simultaneously to characterize the spatiotemporal intensity of scour and sedimentation fluctuations. Combined with the annual slope change rate data derived from the digital elevation model, a set of characteristic parameters for stability assessment is formed. By integrating two characteristic parameters, namely the standard deviation of erosion and sedimentation rate and the annual slope change rate, a noise-resistant stability index is constructed through product operation. The critical threshold of seabed stability is determined by cumulative frequency analysis. Seabed stability is divided into three levels: stable zone, transition zone and unstable zone, and a stability classification assessment system is established. Based on the stability index classification system, the study area is spatially rasterized, the rationality of the partition boundaries is verified by cluster analysis, a spatial distribution map of seabed stability is generated, and regional attribute annotation is performed in conjunction with a geographic information system to clarify the spatial range and stability characteristics of each partition.

[0013] A further improvement to the technical solution of the present invention is that: in step S6, the process of identifying micro-topographical abrupt change zones is as follows: Based on the purified time-series scouring and silting rate data and multi-period high-resolution water depth grid, the data is imported into the programming environment to calculate the topographic curvature value for each year on a pixel-by-pixel basis. Then, the difference in topographic curvature between adjacent years is calculated to obtain the interannual variation of topographic curvature. By integrating the interannual variation of topographic curvature of each pixel over all years, a cumulative topographic curvature variation rate model is constructed. This model no longer reflects the instantaneous changes in a single year, but represents the net effect and continuous trend of topographic curvature changes over the entire observation period. The cumulative topographic curvature change rate model is spatially convolved with a detection operator of a specific scale. The scale of the convolution kernel is matched with the diameter of the submarine pipeline to be protected or the scale of the expected scour pit. The convolution kernel slides across the model data. By capturing high gradient values ​​and abnormal peaks in the convolution results, local areas with drastic changes in cumulative curvature, namely micro-topographic abrupt change zones, are identified within the study area.

[0014] A further improvement to the technical solution of this invention lies in the following: In step S6, the process of formulating targeted engineering protection measures is as follows: The vector data of pipeline alignment is spatially overlaid with the raster data of micro-topographic change zones. Georegistration is performed under a unified coordinate system. The correspondence between pipeline nodes and adjacent change zones is established through spatial association algorithms. The mean cumulative curvature change rate and scouring and silting trend slope of the area covered by each pipeline segment are extracted. The field association between the pipeline attribute table and the dynamic parameters of the terrain is completed, forming the basic dataset for risk coupling analysis. Based on the basic dataset of risk coupling analysis, a threshold for cumulative curvature change rate and a scouring and silting trend discrimination rule are set. When a pipeline segment simultaneously meets the conditions of high curvature change and continuous scouring trend, it is marked as a potential suspended segment. A spatial clustering algorithm is used to aggregate adjacent high-risk segments. The suspended probability is calculated by combining pipeline burial depth and soil parameters, and a thematic layer of pipeline risk classification with spatial continuity is generated. Based on the pipeline risk classification results, differentiated protection designs are implemented. Rigid protection projects are planned for high-risk sections and construction boundaries are determined. Monitoring equipment is deployed for medium-risk sections and inspection frequencies are set. Low-risk sections retain natural recovery conditions and only conduct annual inspections. Then, an engineering protection measure deployment map is output, clarifying the plane coordinates and implementation priorities of various measures.

[0015] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: 1. This invention provides a method for analyzing the evolution of underwater delta erosion and deposition based on water depth data. By integrating multi-temporal remote sensing data with historical transect water depth data, a continuous spatiotemporal database covering the entire lifecycle from construction to abandonment is constructed. Combining seven-parameter coordinate transformation and Kriging interpolation techniques, the method effectively fills in data gaps and generates a continuous water depth surface with a spatial resolution of ≤50m. This method overcomes the limitations of dynamic monitoring caused by data gaps in traditional research, enabling the analysis of erosion and deposition evolution to shift from fragmented observation to systematic tracking, and providing data support for accurately assessing seabed stability.

[0016] 2. This invention provides a method for analyzing the evolution of underwater delta scour and sedimentation based on water depth data. Based on the sliding window method and linear time series fitting, the annual average scour and sedimentation rate is calculated pixel by pixel to distinguish between sedimentation and scour areas. The scour and sedimentation volume is quantified by spatial integration. Combined with the analysis of water depth standard deviation and slope change rate, the spatial heterogeneity and dynamics of topography are further revealed. This not only improves the objectivity of judging scour and sedimentation trends, but also locates high-risk scour or sedimentation hotspots, providing a scientific basis for the targeted implementation of engineering protection measures. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a schematic diagram illustrating the workflow of the underwater delta erosion and deposition evolution analysis method based on water depth data according to the present invention. Figure 2 This is a schematic diagram of the process for analyzing the evolution of underwater delta erosion and deposition based on water depth data, according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, such as Figure 1 , Figure 2As shown, this invention provides a method for analyzing the evolution of underwater delta erosion and deposition based on water depth data, comprising the following steps: S1. Collect historical transect water depth data, remote sensing data, and hydrological data of the underwater delta in the study area. Preprocess the data to unify the coordinate system and elevation datum, remove outliers, and ensure data consistency. Collect historical transect water depth data, multi-source remote sensing data, and related hydrological data of the underwater delta in the study area. Perform preliminary classification and organization of the scattered data to ensure complete time series coverage, encompassing both the construction and post-abandonment phases. Data sources include water resources departments, satellite platforms, and meteorological agencies. Verify the spatiotemporal resolution and reliability of the data. Process all collected data according to unified standards. The coordinate system transformation unifies data from different sources to the same coordinate system and elevation datum, eliminating data deviations caused by datum differences. The same coordinate system is the Gauss-Kruger projection, and the elevation datum is the 1985 National Elevation Datum. Geometric correction is performed on remote sensing data, and water depth data is interpolated to the same grid to ensure spatial alignment. During the processing, the projection transformation accuracy needs to be checked to avoid coordinate offset. Outliers are detected in the unified data and removed to ensure data quality. Data consistency is checked through cross-validation, and problematic data is corrected to generate a standardized dataset. The specific work involves: comprehensively collecting historical data on the underwater delta in the study area, including measured cross-sectional water depth data, multi-source remote sensing data, and relevant hydrological data; conducting preliminary classification and organization of the scattered data to ensure complete time series coverage, including data from both the construction and post-abandonment phases; and utilizing data sources from water resources departments, satellite platforms, and meteorological agencies. During the collection process, the spatiotemporal resolution and reliability of the data are verified to ensure data quality from the source and avoid data issues affecting the accuracy of the research results. For the collected data, coordinate system transformation is performed according to a unified standard to unify data from different sources to Gaussian-... The Kruger projection was adopted, and the 1985 National Height Datum was used to eliminate data bias caused by datum differences. Geometric correction was performed on the remote sensing data to ensure its geometric accuracy; water depth data was interpolated to the same grid to ensure accurate spatial alignment of various data types. During processing, the projection transformation accuracy was checked to prevent coordinate shifts and other problems, ensuring the accuracy and consistency of the data in spatial location; outlier detection was performed on the uniformly processed data, and detected outliers were removed to ensure data quality. Cross-validation was used to check data consistency, and problematic data was corrected, thus generating a standardized dataset. S2. Using preprocessed measured water depth data, a water depth database for the Diaokou Leaf-shaped underwater delta is established, covering all stages from the construction period to the post-abandonment period. The preprocessed measured water depth data from previous years are uniformly formatted, including coordinate alignment, unit conversion, and metadata annotation. A spatial database architecture is adopted, and data tables are designed to store year, location, water depth value, and source information. Spatial indexes are simultaneously established to support efficient queries, ensuring data traceability and integrity. The measured water depth data from previous years are integrated according to time series, classified into construction period and post-abandonment stages, and divided into stage sub-databases. Missing data are filled through spatiotemporal interpolation, and consistency is verified twice to generate a standardized grid dataset with a resolution of 200m. A database version management system is established to record the update time, modification content, and responsible person for each update. A data interface is designed to support the automatic import and format adaptation of subsequent newly added measurement data, ensuring the long-term availability of the database. The specific work involves: standardizing and converting the preprocessed historical cross-sectional measured water depth data into a standardized format; unifying all data to the Gauss-Kruger projection and the 1985 National Height Datum through coordinate alignment to eliminate spatial deviations caused by coordinate system differences; simultaneously completing unit conversion to ensure water depth values ​​are uniformly in meters; and standardizing data precision by retaining two decimal places. A spatial database architecture is then constructed, with core data tables containing fields such as year, cross-sectional location (latitude and longitude), water depth value, measurement equipment type, data source institution, and collection date. Metadata tables are also added to record processing logs and quality labeling information. Spatial indexing technology is used for rapid location of cross-sectional points, supporting efficient queries by spatial or temporal range, ensuring data traceability to the original collection source. Based on the standardized cross-sectional data, sub-databases are constructed according to time series, categorizing the construction period (engineering implementation stage) and the post-abandonment stage (natural evolution stage), clearly defining the time boundaries of each stage. For missing years or cross-sections, spatiotemporal kriging interpolation is used, combining data from adjacent years and spatial correlations. A model was developed to generate a regular grid depth dataset with a resolution of 200m, ensuring full coverage of the study area (between 38°05′36′′ and 38°18′35′′N, and 118°38′19′′ and 118°57′27′′E). During interpolation, grid accuracy was evaluated through cross-validation, requiring the root mean square error to be less than 0.5 meters. For areas with deviations exceeding the limit, the interpolation parameters were readjusted. After interpolation, the entire sequence of grid data was validated a second time, comparing the consistency between the original cross-section points and the interpolation results, correcting local outliers, and ultimately forming a standardized depth grid product that is temporally continuous and spatially consistent. A database version management system was established, using a dual identification mechanism of timestamps and version numbers to record the time of each data update, the content of the modifications, and the responsible person's information, ensuring that historical versions are traceable. A data interface module was designed to support the automatic import of new measurement data through standardized formats (CSV, GeoJSON, etc.). The interface has built-in format validation rules including coordinate system detection and unit verification, automatically adapting to data from different sources and triggering preprocessing procedures. S3. Based on remote sensing data, edge detection is performed to extract historical low tide lines as shoreline benchmarks. The evolution process of the shoreline of the Diaokou Leaf-shaped water delta is analyzed, and the shoreline evolution stages are divided. Landsat series remote sensing data are extracted from the preprocessed remote sensing data, and radiometric and geometric corrections are performed to eliminate the effects of atmospheric scattering and topographic distortion. Band combination is used to enhance the contrast between the water and land boundaries. The boundary between the water body and the land is identified based on the edge detection algorithm. Historical low tide lines are extracted as shoreline benchmarks to ensure spatial consistency. Based on the extracted low tide line data, the shoreline position offset, erosion / siltation rate and curvature changes in different years are calculated to obtain shoreline evolution characteristics including shoreline change rate, spatial displacement direction and shoreline curvature. Combined with K-means clustering analysis, multi-temporal shoreline data is extracted to divide the shoreline evolution stages and clarify the time boundaries and dominant dynamic mechanisms of each shoreline evolution stage. The shoreline evolution stages include the stable period, erosion period and siltation period. Furthermore, the process of clarifying the time boundaries and dominant driving mechanisms of each shoreline evolution stage is as follows: This study integrates multi-temporal shoreline data to extract shoreline evolution characteristics reflecting the evolving state, including shoreline change rate (EPR), spatial displacement direction (seaward / landward), and shoreline curvature (radius of curvature). Each shoreline evolution characteristic is normalized to eliminate dimensional differences. Principal component analysis (PCA) is used for dimensionality reduction, retaining principal components with an explanatory power ≥85%. A feature matrix suitable for cluster analysis is constructed, with rows representing years and columns containing variables such as shoreline change rate, spatial displacement direction, and shoreline curvature. The optimal number of clusters (K=3) is determined based on the elbow rule and silhouette coefficient method, corresponding to the stable period, erosion period, and siltation period, respectively. K-mea is used. The ns algorithm iteratively optimizes the feature matrix, classifies shoreline status by minimizing the sum of squared intra-class distances, and combines clustering results with time series to clarify the start and end years of each stage, generate a distribution map of shoreline evolution stages, and verify the spatiotemporal continuity of the classification boundary. It integrates hydrological, meteorological, and human activity data, correlates external driving factor data such as water and sediment flux, wave energy, and human activity intensity, and analyzes the dominant dynamic mechanism of each shoreline evolution stage. Among them, the siltation stage is associated with high sediment transport and low wave energy, the erosion stage corresponds to the cutoff of sediment sources and strong wave action, and the stable stage matches artificial shoreline stabilization or natural equilibrium state, ultimately forming a stage-dynamic coupling shoreline evolution interpretation framework. The expression for the rate of change of the shoreline is as follows: ; In the formula: EPR is the rate of change of the shoreline, which represents the change of the shoreline per unit time, and the unit is meters per year (m / yr). It is used to quantify how fast the shoreline changes over time. The difference in vertical distance between the shoreline and the baseline in adjacent years reflects the change in the position of the shoreline relative to a fixed baseline in the vertical direction at different points in time. It is obtained by measuring the vertical distance between the shoreline and the baseline in different years and calculating the difference. The time interval is measured in years, which is the time difference between two adjacent measurement years. The expression for the direction of spatial displacement is as follows: ; In the formula: D is a direction variable, used to represent the direction of spatial displacement of the shoreline. When advancing towards the sea, D takes a value of +1, and when retreating towards the land, D takes a value of -1; is a sign function, a mathematical function. For any real number x, when x>0, When x=0, When x < 0, The sedimentation period is characterized by high EPR (>1.5 m / year), a seaward orientation (+1), and low curvature (radius > 1 km); the erosion period is characterized by low EPR (<-1.5 m / year), a landward orientation (-1), and high curvature (radius < 500 m); the stable period is... <0.5m / year, moderate curvature (500~1000m); The specific work involved performing radiometric and geometric corrections on the acquired Landsat series remote sensing data to eliminate the effects of atmospheric scattering and topographic distortion. Radiometric correction employed the FLAASH model, using the remote sensing data acquisition time, solar altitude angle, and atmospheric parameters to eliminate interference from aerosols and Rayleigh scattering, ensuring that pixel reflectance accurately reflects surface features. Geometric correction, based on ground control points (GCPs) and a digital elevation model (DEM), used a polynomial transformation model to register the remote sensing data to a unified coordinate system, controlling the residuals to within 0.5 pixels. A combination of near-infrared (NIR) and shortwave infrared (SWIR) bands was selected, and a normalized water index was used to highlight water body information. To enhance the contrast between the water and land boundaries, an edge detection algorithm based on the Canny operator is used to extract the water-land boundary line. Combined with tide table data to correct for the impact of instantaneous water levels, the low tide line is extracted annually as the shoreline benchmark to ensure spatial consistency. Based on the extracted low tide line data, the spatial offset of the shoreline position in different years is calculated. A shoreline change rate map is generated using a Digital Shoreline Analysis System (DSAS). The erosion / siltation rate is quantified using the Endpoint Rate (EPR) method and Net Shoreline Movement (NSM), analyzing the spatial displacement direction of shoreline changes and distinguishing between areas advancing seaward (siltation) and retreating landward (erosion). Simultaneously, the shoreline curvature, i.e., the radius of curvature (unit: m), is calculated and analyzed using ArcGIS. The Line Curvature tool calculates the radius of the fitted circle for each 1km shoreline segment, analyzes the spatiotemporal evolution of shoreline morphology, and uses spatial statistical methods to examine the clustering or dispersion of shoreline changes, identifying hotspot erosion zones and stable zones. Combined with K-means clustering analysis, multi-temporal shoreline data is analyzed, using shoreline change rate, spatial displacement direction, and shoreline curvature as clustering variables to classify the phased patterns of shoreline evolution, including stable, erosion, and siltation periods. The rationality of the clustering results is verified using the silhouette coefficient, clarifying the temporal boundaries of each shoreline evolution stage. Based on data on water and sediment flux, wave energy, and human activities, the dominant dynamic mechanisms of different stages are analyzed. Specifically, the siltation period is mainly driven by river sediment transport, the erosion period is affected by wave scouring and sediment source depletion, and the stable period reflects natural adjustment or artificial shoreline stabilization. S4. Combining the water depth database and shoreline evolution stages, the scour and deposition evolution stages of the Diaokou leaf-shaped underwater delta are divided, and the evolution characteristics of each scour and deposition evolution stage are identified. Multi-temporal shoreline data and water depth database are integrated, a unified coordinate system is used, and missing values ​​are processed by interpolation. A spatiotemporal continuous dataset covering the Diaokou leaf-shaped underwater delta is constructed, and shoreline positions and water depth profiles for each year are extracted. The changes in underwater topographic elevation are calculated, and the rate of water depth change is determined. Based on the shoreline evolution stages (stable period / erosion period / deposition period) and the rate of water depth change, the scour and deposition evolution stages of the underwater delta are divided through cluster analysis. The spatial distribution characteristics of each scour and deposition evolution stage, including the standard deviation of water depth and the annual rate of slope change, are calculated. Among them, the scour and deposition evolution stages are divided into the construction period (rapid deposition), the abandonment period (rapid scouring), and the adjustment period (alternating scour and deposition). Combining the spatial distribution characteristics, the evolution characteristics of each scour and deposition evolution stage are identified, and the critical threshold for scour and deposition transition is determined. The specific work involves: integrating multi-temporal shoreline data and water depth databases, unifying them into a unified coordinate system, using a seven-parameter transformation method to eliminate systematic bias, performing cubic spline interpolation to complete missing shoreline data segments based on shoreline morphology from adjacent years, and generating continuous water depth surfaces with a spatial resolution ≤50m by combining Kriging interpolation and weighted averaging with nearby sounding points for missing water depth data areas. Then, through time series alignment, a spatiotemporal continuous dataset covering the Diaokou-Yeban underwater delta is constructed, including shoreline locations (vectors) and water depth profiles (raster) for each year. The shoreline locations and corresponding water depth profiles for each year are extracted, and the changes in underwater topographic elevation between adjacent years are calculated. The water depth change rate is determined by fitting the time series data with linear regression. Finally, K-means clustering analysis is used to classify the underwater delta based on the shoreline evolution stages (stable period / erosion period / siltation period) and the water depth change rate. Sedimentation Evolution Stages: Using the standard deviation of water depth (reflecting spatial heterogeneity) and the annual rate of change of slope (dynamic topographic steepness) as spatial distribution characteristics, the study area was divided into construction, abandonment, and adjustment stages, achieving a quantitative stage division of the scour and sedimentation process. Based on the spatial distribution characteristics and clustering results of each scour and sedimentation evolution stage, the evolution characteristics of each stage were analyzed. Among them, the construction stage was mostly distributed in the front edge of the estuary sandbar, characterized by shallower water depth and gentler slope; the abandonment stage was concentrated in abandoned river channels and offshore sand bodies, characterized by deeper water depth and steeper slope; the adjustment stage was mostly seen in the scour-sedimentation transition zone, with frequent topographic fluctuations. By statistically analyzing the cumulative distribution function (CDF) of water depth change rate and topographic parameters for each stage, the critical threshold for scour-sedimentation transition was determined (the threshold for water depth change rate from construction stage to adjustment stage is 0.2 m / yr, and the threshold for water depth change rate from abandonment stage to adjustment stage is -0.15 m / yr). The formula for calculating the change in underwater topographic elevation between adjacent years is as follows: ; In the formula: Let be the water depth value in year t. Let be the water depth value in year t+1. This represents the change in underwater topographic elevation between adjacent years. >0 indicates siltation, reduced water depth, and topographic uplift; <0 indicates erosion, where water depth increases and the terrain is eroded; =0 indicates stable terrain; The expression for calculating the rate of change of water depth is as follows: ; In the formula: V is the annual rate of change of water depth. The time interval is defined as follows: V>0 represents the average annual siltation rate, V<0 represents the average annual erosion rate, and V approaching 0 indicates dynamic equilibrium of the terrain. The formula for calculating the standard deviation of water depth is as follows: ; ; In the formula: The standard deviation of water depth reflects the spatial heterogeneity of topography. Let i be the water depth value of the i-th grid cell. The value represents the average water depth across all grid cells in the study area, where n is the total number of grid cells. Larger terrains are characterized by dramatic undulations. The terrain is flat and uniform; The formula for calculating the annual rate of change of slope is as follows: ; ; In the formula: Annual slope variation rate (unit: o / yr), reflecting the dynamics of terrain steepness and gentleness, where m is the total number of profiles. , Let be the slope values ​​of the j-th profile in year t and year t+1. The slope of a single grid cell. , The gradient of water depth z in the x (east-west) and y (north-south) directions (calculated via DEM differencing) is given. >0 indicates that the terrain becomes steeper. <0 indicates a gentler terrain; during the construction period, Positive values ​​predominate (accumulation), with V being positive (rapid accumulation). Smaller (uniform deposition), Negative or zero (terrain flattening); abandoned period, Predominantly negative values ​​(erosion), with V being negative (rapid erosion). It may increase (coexistence of erosion pits and residual sand bodies). High positive value (steepened terrain); adjustment period, The alternation of positive and negative values ​​(balance between scouring and sedimentation) occurs, with V approaching zero or fluctuating significantly. Larger (strong spatial heterogeneity), For fluctuations (dynamic terrain adjustment); S5. Calculate the scouring and sedimentation volume and scouring rate at each stage of scouring and sedimentation evolution. Use a wavelet transform-hidden Markov model hybrid algorithm to purify the scouring and sedimentation rate data, analyze the scouring and sedimentation evolution trend, and evaluate seabed stability. S6. Based on the purified siltation rate data, construct a cumulative topographic curvature change rate model to identify micro-topographic abrupt change zones and potential pipeline suspension sections, and formulate targeted engineering protection measures.

[0021] Example 2, as Figure 1 , Figure 2 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in S5, the process of purifying the scouring and sedimentation rate data using a wavelet transform-hidden Markov model hybrid algorithm is as follows: Based on the spatiotemporally continuous water depth data in the water depth database, the underwater topographic elevation change in adjacent years is calculated pixel by pixel, and the scouring and sedimentation volume of each scouring and sedimentation evolution stage is quantified by spatial integration. The annual average scouring rate is calculated using the sliding window method to distinguish between sedimentation and scouring areas. Combined with the scouring and sedimentation stage classification results, the total scouring and sedimentation volume of each scouring and sedimentation evolution stage is statistically analyzed to form a time-series scouring and sedimentation rate dataset covering the study area. Wavelet transform is applied to the time-series scouring and sedimentation rate data of each pixel for multi-scale decomposition to extract the fluctuation components at different time scales. Hidden Markov models are used to identify the state of the decomposed signal. The true scouring and sedimentation trend and the pseudo signal generated by short-term turbidity current disturbance are distinguished by calculating the state transition probability, and the purified scouring and sedimentation rate dataset is output. The specific work involves: Based on the established spatiotemporal continuous water depth database, calculations are performed in GIS software. Operators use individual pixels (50m×50m grids) as the basic unit, programming to batch calculate the changes in underwater topographic elevation between adjacent years. Using spatial integration tools, the elevation change of each pixel is multiplied by its area and summed to quantify the scour and deposition volume of the entire study area at different stages of scour and deposition evolution. Positive and negative values ​​are used to distinguish between depositional and scour areas. A sliding window method (setting a 5-year window width) is used to calculate the annual average scour and deposition rate of each pixel year by year, ultimately generating a raster dataset covering the entire study area and containing multi-year series data—a time-series scour and deposition rate dataset covering the study area. To address the short-term noise mixed in the time-series data downloaded from the study area's strong tidal and high turbidity environment, a wavelet transform-Hidden Markov Model (WT-HMM) hybrid algorithm is used. Through programming (using Python's PyWavelets library), wavelet transforms are applied to the time-series scour and deposition rate data of each pixel, decomposing it into fluctuation components at different time scales. This study covers long-term trends, interannual cycles, and short-term transient fluctuations caused by turbidity currents. Two hidden states are predefined: the true scouring and silting state and the noise disturbance state. The Hidden Markov Model (HMM) is trained using historical data, enabling the model to learn the output probabilities of each state and the state transition probabilities between them. The component sequences obtained from wavelet decomposition are input into the trained HMM. The HMM uses the Viterbi algorithm to calculate the hidden state sequence and calculates the state transition probability, i.e., the probability of the system switching from one state to another, through the forward-backward algorithm. Based on the state sequence and transition probability, a judgment is made. The persistent true scouring and silting trend is characterized by long-term residence in the true scouring and silting state and a very high probability of state transition, showing strong persistence. Conversely, the short-lived and negligible pseudo-signals are characterized by frequent and short-lived jumps to the noise disturbance state, and their state transition probabilities show extremely high instability. Thus, the transient fluctuation components marked as noise disturbance states are automatically removed from the original signal, and the purified scouring and silting rate dataset is output. In S5, the process of analyzing the evolution trend of scour and sedimentation and assessing seabed stability is as follows: Based on the purified scour and sedimentation rate dataset, the slope of the scour and sedimentation trend is calculated pixel by pixel using a linear time series fitting method. The long-term evolution direction is quantified by the least squares method, and the evolution patterns of accelerated scour, slowed sedimentation, and equilibrium are identified. The standard deviation of the scour and sedimentation rate of each pixel during the observation period is extracted simultaneously to characterize the spatiotemporal intensity of scour and sedimentation fluctuations. Combined with the annual slope change rate data derived from the digital elevation model, a set of characteristic parameters for stability assessment is formed. The two characteristic parameters, the standard deviation of the scour and sedimentation rate and the annual slope change rate, are integrated and a noise-resistant stability index is constructed through product operation. The critical threshold of seabed stability is determined by the cumulative frequency analysis method. Seabed stability is divided into three levels: stable zone, transition zone, and unstable zone. A stability classification assessment system is established. Based on the stability index classification system, the study area is spatially rasterized. The rationality of the partition boundaries is verified by cluster analysis. A spatial distribution map of seabed stability is generated. Regional attribute labeling is performed in conjunction with the geographic information system to clarify the spatial range and stability characteristics of each partition. The specific work involves: after obtaining the purified erosion and deposition rate dataset, linear time series fitting of the temporal erosion and deposition rate for each pixel (50m×50m grid cell) within the study area is performed on a numerical computing platform (Python's NumPy). The erosion and deposition trend slope of each pixel is accurately calculated using the least squares method to quantify the long-term evolution direction of the seabed during the observation period (nearly 10 years): negative values ​​represent continuous erosion, and positive values ​​represent continuous deposition. Simultaneously, the standard deviation of the erosion and deposition rate for each pixel throughout the time series is calculated to characterize the intensity of erosion and deposition fluctuations at that location. The annual slope variation rate for each pixel is extracted from a multi-year digital elevation model (DEM) to reflect the dynamic adjustment of underwater topography. The erosion and deposition trend slope, standard deviation of the erosion and deposition rate, and annual slope variation rate are jointly constructed into a feature parameter set for stability assessment. Based on the feature parameter extraction, the standard deviation of the erosion and deposition rate, representing the intensity of fluctuations, is fused with the annual slope variation rate, reflecting topographic dynamics, through product operations to construct a resistance... The stability index of the noise level comprehensively reflects the dynamic instability of the terrain. The higher the value, the greater the risk. The cumulative frequency analysis method is used to statistically analyze the stability index of all pixels. By finding the inflection point on the cumulative frequency curve, the critical threshold for classifying the seabed stability level is determined, thereby dividing the seabed stability of the entire study area into three levels: stable zone (low stability index, gradual change), transition zone (medium stability index, uncertainty), and unstable zone (high stability index, drastic change), thus establishing a stability classification assessment system. Using the raster calculation and reclassification functions of the geographic information system, each pixel is classified into the corresponding stability level according to its stability index value, automatically generating a spatial distribution map of seabed stability. K-means clustering analysis is further used to perform machine learning on the feature parameter set to verify whether the categories classified by the machine match the manually set threshold partitions on the spatial boundary, verifying the rationality of the automatic partitioning results. Finally, after the map is generated, attribute labels are added to each partition. The formula for calculating the volume of siltation and sedimentation is as follows: ; In the formula: The volume represents sedimentation; positive values ​​indicate sedimentation, and negative values ​​indicate erosion. Let A be the elevation change of the g-th pixel from year t to year t+1, A be the pixel area, R be the total number of years within the period, E be the total number of pixels in the study area, and t be the time variable, representing the construction period. >0 and continues to increase; abandonment period <0 and the absolute value increases; adjustment period Approaching 0, with small fluctuations; The formula for calculating the annual average scour rate is as follows: ; In the formula: For pixels The average annual scouring and silting rate during the window period, where k is the sliding window width. For pixels The change in elevation in year s; The expression for calculating the slope parameter is as follows: ; In the formula: The slope parameter represents the pixel. The long-term scouring and sedimentation trend slope accelerates scouring. <-0.05; Reduces sedimentation >0.03; Balance ; The formula for calculating the standard deviation of the scouring and silting rate is as follows: ; In the formula: The standard deviation of the siltation rate is represented by the pixel. The spatiotemporal fluctuation intensity of scouring and silting rates, For pixels The multi-year average scouring and silting rate, the stable zone Transition zone Unstable region ; The expression for calculating the stability index is as follows: ; In the formula: SI is the stability index; the larger the value, the more unstable the system. The annual rate of change of slope indicates a stable zone (low risk). Transition Zone (Medium Risk) Stable zone (high risk) ; In S6, the process of identifying micro-topographic abrupt change zones is as follows: Based on the purified time-series scour and sedimentation rate data and multi-period high-resolution water depth grid, the data is imported into the programming environment. The topographic curvature value for each year is calculated pixel by pixel. The topographic curvature value is quantified by the second derivative operation to determine the degree of curvature of the seabed surface. Then, the difference in topographic curvature between adjacent years is calculated to obtain the interannual variation of topographic curvature. The interannual variation of topographic curvature of each pixel in all years is integrated over time to construct a cumulative topographic curvature change rate model. This model no longer reflects the instantaneous changes in a single year but represents the net effect and continuous trend of topographic curvature changes throughout the entire observation period. The cumulative topographic curvature change rate model is spatially convolved with a detection operator of a specific scale. The scale of the convolution kernel is matched with the diameter of the seabed pipeline to be protected or the expected scale of the scour pit. The convolution kernel slides across the model data. By capturing high gradient values ​​and abnormal peaks in the convolution results, local areas with drastic cumulative curvature changes in the study area, i.e., micro-topographic abrupt change zones, are identified. In S6, the process of developing targeted engineering protection measures is as follows: Vector data of pipeline alignment is spatially overlaid with raster data of micro-topographic abrupt change zones. Geographic registration is performed in a unified coordinate system. A spatial association algorithm is used to establish the correspondence between pipeline nodes and adjacent abrupt change zones. The average cumulative curvature change rate and scour / siltation trend slope of the area covered by each pipeline segment are extracted. Field association between the pipeline attribute table and dynamic topographic parameters is completed, forming the basic dataset for risk coupling analysis. Based on this dataset, a threshold for the cumulative curvature change rate and scour / siltation trend discrimination rules are set. When a pipeline segment simultaneously meets the criteria of high curvature change and continuous scour trend, it is marked as a potential suspended segment. A spatial clustering algorithm is used to aggregate adjacent high-risk segments. The suspended probability is calculated by combining pipeline burial depth and soil parameters. A spatially continuous pipeline risk classification thematic layer is created. Based on the pipeline risk classification results, differentiated protection designs are implemented. Rigid protection projects are planned for high-risk sections, and construction boundaries are determined. Monitoring equipment is deployed for medium-risk sections, and inspection frequencies are set. Low-risk sections are preserved for natural recovery, with only annual inspections. The resulting engineering protection measure deployment map clearly defines the plane coordinates and implementation priorities of various measures. Among them, rigid protection projects are planned for high-risk sections, such as filling with crushed stone cushions, installing concrete pipe stabilizing blocks, or erecting flexible beach protection devices. In the design scheme, the construction boundaries are accurately determined according to the risk range, and the required material volume is calculated. For medium-risk sections, monitoring and early warning are the main focus. Fixed monitoring equipment (acoustic principle-based measurement and scanning systems) is deployed, and a clear inspection frequency (quarterly multi-beam scanning) is set.

[0022] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for analyzing the erosion and deposition evolution of underwater deltas based on water depth data, characterized in that, Includes the following steps: S1. Collect historical measured water depth data, remote sensing data and hydrological data of the underwater delta in the study area, and preprocess them to unify the coordinate system and elevation datum. S2. Using the pre-processed measured water depth data, establish a water depth database for the Diaokou Leaf-shaped underwater delta, covering all stages from the construction period to the post-abandonment period. S3. Based on remote sensing data, edge detection is performed to extract historical low tide lines as shoreline benchmarks, analyze the shoreline evolution process, and divide the shoreline evolution stages. S4. Combining the water depth database with the shoreline evolution stages, the scouring and deposition evolution stages of the Diaokou Leaf-shaped underwater delta are divided and the evolution characteristics of each scouring and deposition evolution stage are identified. S5. Calculate the scour volume and erosion rate of each stage of scour evolution. Use a wavelet transform-hidden Markov model hybrid algorithm to purify the scour rate data, analyze the scour evolution trend and assess seabed stability. This includes: calculating the underwater topographic elevation change of adjacent years pixel by pixel based on spatiotemporally continuous water depth data in the water depth database, quantifying the scour volume of each stage of scour evolution through spatial integration, calculating the annual average erosion rate using the sliding window method, distinguishing between sedimentation and erosion areas, and combining the scour stage classification results to statistically analyze the total scour volume of each stage of scour evolution, forming a time-series scour rate dataset covering the study area. Wavelet transform is applied to the time-series scouring and silting rate data of each pixel for multi-scale decomposition to extract the fluctuation components at different time scales. Hidden Markov model is used to identify the state of the decomposed signal. The true scouring and silting trend is distinguished from the pseudo signal generated by short-term turbidity disturbance by calculating the state transition probability, and the purified scouring and silting rate dataset is output. S6. Based on the purified siltation rate data, construct a cumulative topographic curvature change rate model to identify micro-topographic abrupt change zones and potential pipeline suspension sections, and formulate targeted engineering protection measures. The process of identifying micro-topographic abrupt change zones is as follows: Based on the purified time-series scouring and silting rate data and multi-period high-resolution water depth grid, the topographic curvature value for each year is calculated pixel by pixel, and then the difference in topographic curvature between adjacent years is calculated to obtain the interannual variation of topographic curvature. The interannual variation of topographic curvature of each pixel in all years is integrated over time to construct a cumulative topographic curvature change rate model, which characterizes the net effect and persistent trend of topographic curvature change over the entire observation period. The cumulative topographic curvature change rate model is spatially convolved with a detection operator of a specific scale. The scale of the convolution kernel is matched with the diameter of the submarine pipeline to be protected or the scale of the expected scour pit. The convolution kernel slides across the model data. By capturing high gradient values ​​and abnormal peaks in the convolution results, local areas with drastic changes in cumulative curvature, namely micro-topographic abrupt change zones, are identified within the study area.

2. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 1, characterized in that: S1 specifically includes: We collected historical cross-sectional measured water depth data, multi-source remote sensing data, and related hydrological data of the underwater delta in the study area, and preliminarily classified and organized the scattered data. For the various types of data collected, coordinate system transformation is performed according to a unified standard to unify data from different sources to the same coordinate system and elevation datum. The same coordinate system is the Gauss-Kruger projection, and the elevation datum is the 1985 National Elevation Datum. Geometric correction is performed on remote sensing data, and water depth data is interpolated to the same grid. Outliers are detected in the unified data, removed, and data consistency is checked through cross-validation. Problematic data is corrected to generate a standardized dataset.

3. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 1, characterized in that: In step S2, the process of establishing the water depth database for the underwater delta of the Diaokou leaf-shaped structure is as follows: The preprocessed historical cross-sectional measured water depth data were uniformly formatted, including coordinate alignment, unit conversion and metadata annotation. A spatial database architecture was adopted, and data tables were designed to store year, location, water depth value and source information, and a spatial index was established simultaneously. The measured water depth data of cross sections over the years were integrated according to time series, classified into construction period and post-abandonment stage, and divided into stage sub-databases. Missing data were filled in by spatiotemporal interpolation, and consistency was checked twice to generate a standardized grid dataset with a resolution of 200m. Establish a database version control system to record the update time, modified content, and responsible person for each update.

4. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 1, characterized in that: S3 specifically includes: Landsat series remote sensing data were extracted from the preprocessed remote sensing data, radiometric and geometric corrections were performed, and band combination was used to enhance the contrast between the water and land boundaries. The boundary between water and land was identified based on the edge detection algorithm, and the historical low tide line was extracted as the shoreline reference. Based on the extracted low tide line data, the shoreline position shift, erosion / siltation rate and curvature changes in different years are calculated to obtain shoreline evolution characteristics including shoreline change rate, spatial displacement direction and shoreline curvature. By combining K-means clustering analysis, multi-temporal shoreline data were extracted to divide the shoreline evolution stages, clarifying the time boundaries and dominant dynamic mechanisms of each shoreline evolution stage. The shoreline evolution stages include the stable period, the erosion period, and the siltation period.

5. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 4, characterized in that: The process of clarifying the time boundaries and dominant driving mechanisms of each shoreline evolution stage is as follows: By integrating multi-temporal shoreline data, shoreline evolution characteristics reflecting the evolutionary state are extracted, including shoreline change rate, spatial displacement direction, and shoreline curvature. Each shoreline evolution characteristic is normalized and dimensionality is reduced through principal component analysis to construct a feature matrix suitable for cluster analysis. Rows represent years, and columns contain variables such as shoreline change rate, spatial displacement direction, and shoreline curvature. The optimal number of clusters is determined based on the elbow rule and the contour coefficient method, with K=3, corresponding to the stable period, erosion period and siltation period respectively. The K-means algorithm is used to iteratively optimize the feature matrix. The shoreline status is classified by minimizing the sum of squared intra-cluster distances. Combining the clustering results with the time series, the start and end years of each stage are determined, a distribution map of shoreline evolution stages is generated, and the spatiotemporal continuity of the classification boundary is verified. By integrating hydrological, meteorological, and human activity data, and correlating external driving factors such as water and sediment flux, wave energy, and human activity intensity, the dominant dynamic mechanisms of each shoreline evolution stage are analyzed, ultimately forming a stage-dynamic coupling explanation framework for shoreline evolution.

6. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 5, characterized in that: In S4, the process of identifying the evolutionary characteristics of each stage of scour and sedimentation is as follows: By integrating multi-temporal shoreline data and water depth database, unifying the coordinate system and interpolating missing values, a spatiotemporal continuous dataset covering the Diaokou Leaf-shaped underwater delta is constructed. The shoreline location and water depth profile for each year are extracted, the change in underwater topography elevation is calculated, and the rate of water depth change is determined. Based on the shoreline evolution stages and water depth change rates, the underwater delta's scour and deposition evolution stages are divided by cluster analysis. The spatial distribution characteristics of each scour and deposition evolution stage, including the water depth standard deviation and annual slope change rate, are calculated. The scour and deposition evolution stages are divided into construction period, abandonment period and adjustment period. By combining spatial distribution characteristics, the evolutionary features of each stage of scour and sedimentation are identified, and the critical threshold for scour and sedimentation transition is determined.

7. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 1, characterized in that: In S5, the process of analyzing the evolution trend of erosion and deposition and assessing seabed stability is as follows: Based on the purified scour and sedimentation rate dataset, the slope of the scour and sedimentation trend is calculated pixel by pixel using the linear time series fitting method. The long-term evolution direction is quantified by the least squares method to identify the evolution patterns of accelerated scour, slowed sedimentation and equilibrium. The standard deviation of the scour and sedimentation rate of each pixel during the observation period is extracted simultaneously to characterize the spatiotemporal intensity of scour and sedimentation fluctuations. Combined with the annual slope change rate data derived from the digital elevation model, a set of characteristic parameters for stability assessment is formed. By integrating two characteristic parameters, namely the standard deviation of erosion and sedimentation rate and the annual slope change rate, a noise-resistant stability index is constructed through product operation. The critical threshold of seabed stability is determined by cumulative frequency analysis. Seabed stability is divided into three levels: stable zone, transition zone and unstable zone, and a stability classification assessment system is established. Based on the stability index classification system, the study area is spatially rasterized, the rationality of the partition boundaries is verified by cluster analysis, a spatial distribution map of seabed stability is generated, and regional attribute annotation is performed in conjunction with a geographic information system to clarify the spatial range and stability characteristics of each partition.

8. The underwater delta erosion and deposition evolution analysis method based on water depth data according to claim 1, characterized in that: In S6, the process of developing targeted engineering protection measures is as follows: The vector data of pipeline alignment is spatially overlaid with the raster data of micro-topographic change zones. Georegistration is performed under a unified coordinate system. The correspondence between pipeline nodes and adjacent change zones is established through spatial association algorithms. The mean cumulative curvature change rate and scouring and silting trend slope of the area covered by each pipeline segment are extracted. The field association between the pipeline attribute table and the dynamic parameters of the terrain is completed, forming the basic dataset for risk coupling analysis. Based on the basic dataset of risk coupling analysis, a threshold for cumulative curvature change rate and a scouring and silting trend discrimination rule are set. When a pipeline segment simultaneously meets the conditions of high curvature change and continuous scouring trend, it is marked as a potential suspended segment. A spatial clustering algorithm is used to aggregate adjacent high-risk segments. The suspended probability is calculated by combining pipeline burial depth and soil parameters, and a thematic layer of pipeline risk classification with spatial continuity is generated. Based on the pipeline risk classification results, differentiated protection designs are implemented. Rigid protection projects are planned for high-risk sections and construction boundaries are determined. Monitoring equipment is deployed for medium-risk sections and inspection frequencies are set. Natural recovery conditions are preserved for low-risk sections. Then, a deployment map of engineering protection measures is output, clarifying the plane coordinates and implementation priorities of various engineering protection measures.

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