Tidal flat space-time evolution prediction method and system

Abstract

The invention discloses a tidal flat space-time evolution prediction method and system. The method comprises the following steps: acquiring tidal flat water power and meteorological data; making a tidal flat face truth value data set; extracting a tidal flat surface; extracting a contour line of a tidal flat surface, and obtaining tidal flat elevation data; dividing tidal flat erosion and deposition intensity grades; nonlinear structural features between the hydrodynamic data and the meteorological data are extracted; the hydrodynamic and meteorological data, the nonlinear structural features, the current tidal flat erosion and deposition intensity grade and the neighborhood factor are subjected to normalization processing and then serve as input of a CLN neural network model, then the tidal flat erosion and deposition intensity grade in the next period serves as output of the CLN neural network model, and optimal network parameters are obtained through training; and constructing a CLN neural network-cellular automaton model to obtain a prediction result. According to the method, the long-term nonlinear coupling effect of hydrodynamic force and meteorological factors on the tidal flat can be fully reflected, the tidal flat space-time prediction difficulty is reduced, and the accuracy and robustness of tidal flat space-time evolution prediction are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coastal zone monitoring, relates to the spatio-temporal evolution of tidal flats, and particularly relates to a method and system for predicting the spatio-temporal evolution of tidal flats. Background Technique

[0002] Tidal flats are located at the junction of marine and terrestrial ecosystems and play an important role in maintaining the diversity of coastal ecosystems and weakening extreme storm surges. Due to the influence of hydrodynamic environment and meteorological factors, tidal flats are often submerged by tides and scoured by waves, and their morphology is constantly changing. Predicting the spatio-temporal evolution of tidal flats can provide data support and scientific reference for the sustainable development of tidal flats and the protection of coastal wetlands. The prediction of the spatio-temporal evolution of tidal flats refers to establishing a mathematical model based on the spatio-temporal change amount of tidal flat elevation and the historical observation data of surrounding environmental factors, and predicting the spatial distribution and morphological change trend of tidal flats in a future period.

[0003] Commonly used methods for predicting the spatio-temporal evolution of tidal flats mainly include the method based on the cellular automata-Markov model and the method based on hydrodynamic-sediment numerical simulation. The method based on the cellular automata-Markov model divides tidal flats, the ocean and other lands into cells, uses the weight factors created by the cellular filter, takes the transfer area matrix calculated by the Markov model and the conditional probability map of driving factors obtained by Logistic regression as transfer rules, and combines the states of adjacent cells to predict the tidal flats in the next period. Although this method has the ability to simulate the spatial changes of tidal flats and the advantage of long-term prediction, natural environmental factors such as wind, waves and currents have not been added to the transfer rules of tidal flat evolution. The method based on hydrodynamic-sediment numerical simulation uses a hydrodynamic numerical simulation software package to solve the hydrodynamic equation and sediment transport equation by using basic data such as topography and water depth, and models the sediment transport of tidal flats driven by the hydrodynamic environment, and can simulate the erosion and deposition evolution of tidal flats in a future period. Although this method can effectively simulate the complex scenarios of the interaction of wind, waves and currents, the setting and calibration of the model are difficult, requiring high professional knowledge, and at the same time, the accuracy of the model highly depends on the quality of the input data. Summary of the Invention

[0004] Object of the Invention: In order to overcome the deficiencies in the prior art, the present invention provides a method and system for predicting the spatio-temporal evolution of tidal flats, which can fully reflect the long-term non-linear coupling effect of hydrodynamic and meteorological factors on tidal flats, reduce the difficulty of spatio-temporal prediction of tidal flats, improve the accuracy and robustness of spatio-temporal evolution prediction of tidal flats, and make the prediction results of spatio-temporal evolution of tidal flats more realistic.

[0005] Technical Solution: To achieve the above object, the present invention provides a method for predicting the spatio-temporal evolution of tidal flats, including the following steps:

[0006] S1: Obtain tideland hydrodynamic and meteorological data within a set time period;

[0007] S2: Obtain tideland remote sensing image data and, based on existing tideland waterline products, produce a true value dataset of the tideland surface for training and testing a tideland surface extraction model;

[0008] S3: Use transfer learning to fine-tune a land-sea segmentation deep learning model, and extract the tideland surface from remote sensing images through the fine-tuned model;

[0009] S4: Use morphological operators to extract the contour line of the tideland surface, and then use a tideland elevation extraction method to obtain tideland elevation data;

[0010] S5: Divide the tideland erosion and deposition intensity grades according to the changes in tideland elevation;

[0011] S6: Use the uniform manifold approximation and projection algorithm to extract the non-linear structural features between the hydrodynamic and meteorological data in step S1;

[0012] S7: After normalizing the hydrodynamic and meteorological data in step S1, the non-linear structural features extracted in step S6, the current tideland erosion and deposition intensity grade, and the neighborhood factor, use them as the input of the constructed CLN neural network model, and then use the tideland erosion and deposition intensity grade of the next period as the output of the CLN neural network model for training to obtain the optimal network parameters as the transition rules of the cellular automaton;

[0013] S8: According to the trained CLN neural network model, construct a CLN neural network - cellular automaton model, set the transition threshold, adjust the number of iterations, and obtain the prediction result.

[0014] Further, the method for obtaining tideland hydrodynamic and meteorological data in step S1 is as follows: Determine the longitude and latitude range of the study area and the data accumulation interval time ΔT, obtain hydrodynamic and meteorological data other than tide level and tidal current, and obtain tide level and tidal current data from tidal stations and the global ocean tide model TPXO9;

[0015] Calculate the average wind speed at 10 meters above the sea surface, significant wave height, maximum tidal level difference, average tidal current velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure within the interval time ΔT.

[0016] Further, step S2 specifically includes:

[0017] A1: Obtain the artificial shoreline in the tideland remote sensing image through expert visual interpretation and use it as the upper boundary of the tideland;

[0018] A2: Using the tidal flat remote sensing image as the base map, place the corresponding tidal flat waterline layer and the upper boundary layer of the tidal flat on it. After merging the two layers, a closed area is formed, and this area is filled with white to obtain the true value of the tidal flat surface, while the background is filled with black.

[0019] Furthermore, in step S3, a transfer learning method is used to fine-tune the land-sea segmentation deep learning model, which specifically includes:

[0020] B1: Download the pre-trained weights of the HED-Unet land-sea segmentation deep learning model, and freeze the shallow network of the feature extraction part of the model;

[0021] B2: Adjust the loss function according to the characteristics of the tidal flat surface true value dataset in step S2. The adjusted loss function is:

[0022]

[0023] where N represents the number of image samples input for each training, x n represents the output probability map of the model, y n represents the label information of the sample, and ω n represents the ratio of the true value of the tidal flat surface to the background in the dataset;

[0024] B3: Train the unfrozen feature extraction network layer and the classification part until the accuracy of the model and the loss function are basically convergent.

[0025] In this step, transfer learning is mainly reflected in B1 and B3; model fine-tuning can enable the HED_Unet model to better adapt to the tidal flat surface extraction task, accelerate convergence, and at the same time reduce the training cost.

[0026] Furthermore, step S4 specifically includes:

[0027] C1: Select a rectangular structural element, and perform erosion and dilation operations on the binary image of the tidal flat surface obtained in step S3 respectively. Subtract the result of the dilation operation from the result of the erosion operation to obtain the contour line of the tidal flat surface;

[0028] C2: In the row scanning mode, obtain the position of a pixel point on the contour line. Starting from this point, find the remaining pixel points clockwise or counterclockwise, and record the coordinate information;

[0029] C3: According to the coordinate information, use the tidal flat elevation extraction method to obtain the tidal flat elevation.

[0030] Furthermore, step S5 specifically includes:

[0031] Calculate the change in tidal flat elevation for each period. According to the statistical situation of the change histogram, divide it into 13 levels: severe erosion, extremely high-intensity erosion, high erosion, moderate erosion, slight erosion, very slight erosion, no change, very slight siltation, slight siltation, moderate siltation, high siltation, extremely high-intensity siltation, severe siltation.

[0032] Furthermore, the specific steps of step S6 include:

[0033] D1: Construct a high-dimensional dataset X of hydrodynamic and meteorological data = {x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 , x 9}, and the elements in the dataset are respectively: the average wind speed at 10 meters above the sea surface, the significant wave height, the maximum tidal level difference, the average tidal current velocity, the average sea surface temperature, the average air temperature, the cumulative precipitation, the cumulative evaporation, and the average sea level pressure;

[0034] D2: Initialize the target dimension: Y = {y 1 , y 2 , y 3};

[0035] D3: Construct a weighted graph:

[0036] For each element x i and its nearest neighbor x j , calculate the weight of the edge as:

[0037]

[0038] where d(x i , x j ) is the Euclidean distance between x i and x j , ρ i is the distance from x i to its k nearest neighbors, and σ i is a scale parameter to ensure Σ j w ij = log 2 (k);

[0039] D4: For the points y i and y j in the low-dimensional space, define their similarity:

[0040] z ij = (1 + a · ||y i - y j ||2b ) -1 ,

[0041] wherein, the values of a and b are determined by the minimum distance;

[0042] Using the gradient descent method to minimize the cross-entropy loss function between the high-dimensional and low-dimensional probability distributions:

[0043]

[0044] The finally obtained non-linear structural feature Y:

[0045]

[0046] Furthermore, the CLN neural network model in the step S7 includes: a first convolutional layer module, a second convolutional layer module, an LSTM+Dropout layer, a Dropout layer, and a fully connected layer; the first convolutional layer module and the second convolutional layer module have the same structure, and both include: two convolutions with a kernel size of 3, two ReLU activation functions, and a convolution with a kernel size of 1; wherein, the first convolutional layer module is the input port of the model, which can help the model capture the local dependencies between sequence data such as hydrodynamic and meteorological data, and enhance the local feature representation of a certain type of sequence data under different sea conditions; the second convolutional layer module is connected in series with the first convolutional layer module to form a multi-layer convolutional stacking structure, introducing more complex multi-level non-linear features between sequence data for the model; wherein, the LSTM+Dropout layer can pay more attention to the interaction relationship between short-term hydrodynamic and meteorological data and the interaction relationship within the hydrodynamic data, while improving the robustness of the model when processing a large amount of data; fusing the output results of the LSTM+Dropout layer and the second convolutional layer module enables the following LSTM layer to combine memories at different levels, enhancing the model's ability to capture the characteristics of tidal flat evolution under the action of hydrodynamic and meteorological factors;

[0047] The formula is specifically as follows:

[0048] Let the input of the model be the tensor I, then there is:

[0049] The output of the first convolutional layer module is:

[0050]

[0051] Then through the calculation of the first layer of LSTM:

[0052]

[0053] After the first layer of Dropout processing:

[0054]

[0055] The output of the second convolutional layer module is:

[0056]

[0057] The input of the second LSTM layer undergoes feature fusion:

[0058]

[0059] The output of the second LSTM layer is:

[0060]

[0061] The input of the second Dropout layer undergoes feature fusion:

[0062]

[0063] The output of the second Dropout layer is:

[0064] I o5 = I o4 ·m h

[0065] After full connection calculation, the final output of the CLN model is:

[0066] I CLN = f(W · I o5 + b)

[0067] where W is the weight matrix and b is the bias vector.

[0068] Furthermore, the neighborhood factor Ω in step S7 pq is calculated as follows:

[0069]

[0070] where S pq represents the proportion of various scouring and silting intensity levels in the m × m neighborhood.

[0071] Furthermore, the CLN neural network - cellular automaton model in step S8 is specifically expressed by the formula:

[0072]

[0073] where represents the current tidal flat scouring and silting intensity state, represents the next - period tidal flat scouring and silting intensity state, both represented by the tidal flat scouring and silting intensity levels in step S5; I CLNIt represents the state transition probability, which is calculated by the CLN neural network model; S is the random perturbation factor, representing the uncertainty of the state transition of the tidal flat erosion and deposition intensity; S = 1 + (-lnβ) α , where the value of β ranges from 0 to 1, and the value of α ranges from 0 to 5.

[0074] The present invention also provides a tidal flat spatio-temporal evolution prediction system, including:

[0075] A data acquisition module, which is used to acquire tidal flat hydrodynamic and meteorological data, and tidal flat remote sensing image data;

[0076] A tidal flat information extraction module, which is used to train the land-sea segmentation model by using the transfer learning method according to the collected tidal flat remote sensing images to obtain a tidal flat surface binary image that meets the accuracy requirements. Then, using morphological operators and tidal flat elevation measurement methods, the tidal flat elevation is obtained;

[0077] A data processing module, which is used to calculate the 10-meter average wind speed, effective wave height, maximum tidal level difference, average tidal current velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure on the sea surface; according to the uniform manifold approximation and projection algorithm, obtain their non-linear structural characteristics; calculate the change amount of the tidal flat elevation, and delimit the tidal flat erosion and deposition intensity grade;

[0078] A training module, which is used to input the output data of the data processing module into a pre-constructed CLN neural network model for training to obtain the optimal parameters;

[0079] A prediction module, which is used to use the optimal parameters obtained by the training module as the transition rules of the cellular automaton to predict the spatial change situation of the tidal flat elevation in the next period.

[0080] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0081] 1. The present invention uses tidal flat remote sensing images as the input and trains the land-sea segmentation model by using the transfer learning method to obtain a tidal flat surface binary image that meets the accuracy requirements, which can automatically extract the tidal flat surface area and reduce the time cost and human resources.

[0082] 2. The present invention uses morphological operators and existing tidal flat elevation extraction technologies to automatically extract the tidal flat elevation, calculate the change amount of the elevation, and divide the tidal flat erosion and deposition intensity grade according to the statistical situation of its histogram, which can effectively characterize the erosion and deposition distribution of the tidal flat in space.

[0083] 3. The present invention uses the uniform manifold approximation and projection algorithm to reduce the dimension of the hydrodynamic and meteorological data, and more conveniently finds the non-linear coupling relationship between them.

[0084] 4. The CLN neural network model constructed by the present invention can more clearly highlight the hydrodynamic characteristics that play a dominant role in the evolution of tidal flats in its convolutional layer part, and its long short-term memory mechanism can effectively capture the long-term evolution characteristics of tidal flats under the action of hydrodynamic and meteorological factors.

[0085] 5. The CLN-cellular automaton model constructed by the present invention has its transition rules derived from the parameters of the trained CLN model, which can reflect the driving situation of the natural environment on the evolution of tidal flats, improve the prediction accuracy of the spatio-temporal evolution of tidal flats, and make it more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a schematic structural diagram of the spatio-temporal evolution prediction model of tidal flats of the present invention;

[0087] Figure 2 It is a result map of the extraction of the tidal flat surface of the present invention;

[0088] Figure 3 It is a schematic structural diagram of the CLN neural network of the present invention;

[0089] Figure 4 It is a curve graph of the training and test losses of the CLN neural network of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0090] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification of the present invention fall within the scope defined by the appended claims of this application.

[0091] Example 1:

[0092] As Figure 1 shown, this embodiment provides a method for predicting the spatio-temporal evolution of tidal flats, including the following steps:

[0093] S1: Obtain the hydrodynamic and meteorological data of the tidal flat within a set time period;

[0094] The hydrodynamic and meteorological data of the tidal flat include: 10-meter wind speed on the sea surface, wave height of sea waves, tidal level height, tidal current velocity, sea surface temperature, air temperature, precipitation, evaporation, sea level pressure, etc. The acquisition method is: determine the longitude and latitude range of the research area and the data accumulation interval time ΔT, obtain the hydrodynamic and meteorological data except for tidal level and tidal current, and obtain the tidal level and tidal current data from tidal stations and the global ocean tide model TPXO9;

[0095] Calculate the 10-meter average wind speed, significant wave height, maximum tidal level difference, average tidal current velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure within the interval time ΔT.

[0096] S2: Obtain the remote sensing image data of the tidal flat and, based on the existing tidal flat waterline products, produce a true value dataset of the tidal flat surface for the training and testing of the tidal flat surface extraction model;

[0097] Specifically, it includes the following steps:

[0098] A1: Obtain the artificial shoreline in the tidal flat remote sensing image through expert visual interpretation and use it as the upper boundary of the tidal flat;

[0099] A2: Use the tidal flat remote sensing image as the base map, place the corresponding tidal flat waterline layer and the upper boundary layer of the tidal flat on it. After the two layers are merged, a closed area is formed, and this area is filled with white to obtain the true value of the tidal flat surface, and the background is filled with black.

[0100] S3: Fine-tune the land-sea segmentation deep learning model using transfer learning methods, specifically including:

[0101] B1: Download the pre-trained weights of the HED-Unet land-sea segmentation deep learning model and freeze the shallow network of the feature extraction part of the model;

[0102] B2: Adjust the loss function according to the characteristics of the tidal flat surface true value dataset in step S2. The adjusted loss function is:

[0103]

[0104] where N represents the number of image samples input for each training, x n represents the model output probability map, y n represents the label information of the sample, and ω n represents the ratio of the true value of the tidal flat surface to the background in the dataset;

[0105] B3: Train the unfrozen feature extraction network layer and the classification part until the accuracy of the model and the loss function are basically convergent.

[0106] Extract the tidal flat surface from the remote sensing image through the fine-tuned model. As Figure 2 shown, it is the result of tidal flat surface extraction.

[0107] S4: Use morphological operators to extract the contour line of the tidal flat surface, and then use the tidal flat elevation extraction method to obtain the tidal flat elevation data;

[0108] Specifically, it includes the following steps:

[0109] C1: Select a rectangular structuring element and perform erosion and dilation operations on the binary image of the tidal flat surface obtained in step S3 respectively. Subtract the result of the dilation operation from the result of the erosion operation to obtain the contour line of the tidal flat surface;

[0110] C2: Obtain the position of a pixel point on the contour line in the line scanning mode. Starting from this point, find the remaining pixel points clockwise or counterclockwise, and record the coordinate information;

[0111] C3: According to the coordinate information, use the tidal flat elevation extraction method to obtain the tidal flat elevation.

[0112] S5: Divide the erosion and deposition intensity grades of the tidal flat according to the change of the tidal flat elevation, specifically including:

[0113] Calculate the change amount of the tidal flat elevation for each period. According to the statistical situation of the change amount histogram, divide it into 13 grades: severe erosion, extremely high-intensity erosion, high erosion, moderate erosion, mild erosion, slight erosion, no change, slight deposition, mild deposition, moderate deposition, high deposition, extremely high-intensity deposition, severe deposition, which are represented in number form as: -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6. The erosion and deposition grade division is shown in the following table:

[0114] Table 1 Erosion and Deposition Grade Table

[0115]

[0116] S6: Use the Uniform Manifold Approximation and Projection algorithm to extract the non-linear structural features between the hydrodynamic and meteorological data in step S1;

[0117] Specifically, it includes the following steps:

[0118] D1: Construct a high-dimensional data set X of hydrodynamic and meteorological data = {x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 , x 9}, and the elements in the data set are respectively: the average wind speed at 10 meters above the sea surface, the significant wave height, the maximum tidal level difference, the average tidal current velocity, the average sea surface temperature, the average air temperature, the cumulative precipitation, the cumulative evaporation, the average sea level pressure;

[0119] D2: Initialize the target dimension: Y = {y 1 , y 2 , y 3};

[0120] D3: Construct a weighted graph:

[0121] For each element x i and its nearest neighbor x j , calculate the weight of the edge as:

[0122]

[0123] Among them, d(x i , x j ) is the Euclidean distance between x i and x j . ρ i is the distance from x i to its k nearest neighbors, and σ i is a scale parameter to ensure that ∑ j w ij = log 2 (k);

[0124] D4: For points y i and y j in the low-dimensional space, define their similarity:

[0125] z ij = (1 + a · ||y i - y j || 2b ) -1 ,

[0126] where the values of a and b are determined by the minimum distance;

[0127] Minimize the cross-entropy loss function between the high-dimensional and low-dimensional probability distributions using the gradient descent method:

[0128]

[0129] The finally obtained non-linear structure feature Y:

[0130]

[0131] S7: After normalizing the hydrodynamic and meteorological data in step S1, the non-linear structure features extracted in step S6, the current tidal flat erosion and deposition intensity level, and the neighborhood factor, use them as the input of the constructed CLN neural network model, and then use the tidal flat erosion and deposition intensity level in the next period as the output of the CLN neural network model for training to obtain the optimal network parameters as the transition rules of the cellular automaton;

[0132] The non-linear structure features can capture the complex relationship between hydrodynamic and meteorological data. The neighborhood factor can affect the dynamic behavior of the cellular automaton.

[0133] Such as Figure 3As shown in the figure, the CLN neural network model includes: a first convolutional layer module, a second convolutional layer module, an LSTM + Dropout layer, a Dropout layer, and a fully connected layer; the first convolutional layer module and the second convolutional layer module have the same structure, both including: two convolutions with a convolutional kernel size of 3, two ReLU activation functions, and a convolution with a convolutional kernel size of 1; among them, the first convolutional layer module is the input port of the model, which can help the model capture the local dependencies between hydrodynamic and meteorological sequence data, and enhance the local feature representation of certain types of sequence data under different sea conditions; the second convolutional layer module is connected in series with the first convolutional layer module to form a multi-layer convolutional stacking structure, introducing more complex multi-level non-linear features between sequence data for the model; among them, the LSTM + Dropout layer can pay more attention to the interaction relationships between short-term hydrodynamic and meteorological data and the interaction relationships within hydrodynamic data, while improving the robustness of the model when processing a large amount of data; the output results of the LSTM + Dropout layer and the second convolutional layer module are fused, enabling the following LSTM layer to combine memories at different levels and enhancing the model's ability to capture the tidal flat evolution characteristics under the action of hydrodynamic and meteorological factors.

[0134] The formula is expressed as follows:

[0135] Let the input of the model be the tensor I, then there is:

[0136] The output of the first convolutional layer module is:

[0137] I o1 = f 1×1 (Relu(f 3×3 (Relu(f 3×3 (I)))))

[0138] Then, through the calculation of the first layer of LSTM:

[0139]

[0140] After the first layer of Dropout processing:

[0141]

[0142] The output of the second convolutional layer module is:

[0143]

[0144] The input of the second layer of LSTM undergoes feature fusion:

[0145]

[0146] The output of the second layer of LSTM is:

[0147]

[0148] The input of the second-layer Dropout undergoes feature fusion:

[0149]

[0150] The output of the second-layer Dropout is:

[0151] I o5 = I o4 · m h

[0152] After the fully connected calculation, the final output of the CLN model is:

[0153] I CLN = f(W · I o5 + b)

[0154] where W is the weight matrix and b is the bias vector.

[0155] The neighborhood factor Ω pq is calculated as follows:

[0156]

[0157] where S pq represents the proportion of various scouring and silting intensity levels in the m × m neighborhood.

[0158] As Figure 4 shown, it is the training and test loss curves of the CLN neural network of the present invention. The model can effectively converge on both the training set and the test set and shows stable performance.

[0159] S8: According to the trained CLN neural network model, construct a CLN neural network - cellular automaton model, set the conversion threshold (the conversion threshold is the critical condition for determining the cell state transition, set by the maximum membership principle), adjust the number of iterations, and obtain the prediction result;

[0160] The CLN neural network - cellular automaton model is specifically expressed by the formula:

[0161]

[0162] where L t i represents the current tidal flat scouring and silting intensity state, represents the next-period tidal flat scouring and silting intensity state, both represented by the tidal flat scouring and silting intensity levels in step S5; I CLN represents the state transition probability, calculated by the CLN neural network model; S is the random perturbation factor, expressing the uncertainty of the tidal flat scouring and silting intensity state transition; S = 1 + (-lnβ) α, where the value of β ranges from 0 to 1, and the value of α ranges from 0 to 5.

[0163] Embodiment 2:

[0164] To implement the tidal flat spatio-temporal evolution prediction method of Embodiment 1, this embodiment provides a tidal flat spatio-temporal evolution prediction system, including:

[0165] A data acquisition module, configured to acquire tidal flat hydrodynamic and meteorological data, and tidal flat remote sensing image data;

[0166] A tidal flat information extraction module, configured to train a land-sea segmentation model using a transfer learning method according to the collected tidal flat remote sensing images to obtain a tidal flat surface binary image that meets the accuracy requirements. Then, using morphological operators and tidal flat elevation measurement methods, the tidal flat elevation is obtained;

[0167] A data processing module, configured to calculate the 10-meter average wind speed, significant wave height, maximum tidal level difference, average tidal current velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure on the sea surface; obtain their non-linear structural characteristics according to the uniform manifold approximation and projection algorithm; calculate the change amount of the tidal flat elevation, and delimit the tidal flat erosion and deposition intensity grades;

[0168] A training module, configured to input the output data of the data processing module into a pre-constructed CLN neural network model for training to obtain the optimal parameters;

[0169] A prediction module, configured to use the optimal parameters obtained by the training module as the transition rules of the cellular automaton to predict the spatial change of the tidal flat elevation in the next period.

Claims

1. A method for predicting the spatiotemporal evolution of tidal flats, characterized in that: The steps include: S1: Obtain tidal flat hydrodynamic and meteorological data within a set time period; S2: Obtain tidal flat remote sensing image data and produce a tidal flat surface true value dataset based on the existing tidal flat water edge products for training and testing the tidal flat surface extraction model; S3: Use transfer learning to fine-tune the deep learning model for land-sea segmentation, and use the fine-tuned model to extract tidal flats from remote sensing images; S4: Use morphological operators to extract the contour of the tidal flat surface, and then use the tidal flat elevation extraction method to obtain the tidal flat elevation data; S5: Classify the intensity of tidal flat erosion and deposition according to the change of tidal flat elevation; S6: extracting the nonlinear structural features between the hydrodynamic and meteorological data in step S1 using uniform manifold approximation and projection algorithm; S7: taking the hydrodynamic and meteorological data of step S1, the nonlinear structural features extracted in step S6, the current tidal flat scouring and silting intensity level and the neighborhood factor after normalization as the input of the constructed CLN neural network model, and then taking the tidal flat scouring and silting intensity level of the next period as the output of the CLN neural network model for training to obtain the optimal network parameters as the conversion rules of the cellular automaton; S8: Based on the trained CLN neural network model, a CLN neural network-cellular automaton model is constructed, the conversion threshold is set, the number of iterations is adjusted, and the prediction results are obtained.

2. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: The method for obtaining the tidal flat hydrodynamic and meteorological data in step S1 is as follows: determining the latitude and longitude range of the study area and the data accumulation interval ΔT, obtaining the hydrodynamic and meteorological data except the tide level and tidal current, the tide level and tidal current data are obtained from the tidal station and the global ocean tide model TPXO9; Calculate the average wind speed at 10 meters above the sea surface, significant wave height, maximum tidal range, average tidal velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure within the interval time ΔT.

3. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: The step S2 specifically includes: A1: Obtain the artificial shoreline in the tidal flat remote sensing image through expert visual interpretation and use it as the upper boundary of the tidal flat; A2: Using the tidal flat remote sensing image as the base map, place the corresponding tidal flat water edge layer and the tidal flat upper boundary layer on it. After the two layers are merged, a closed area is formed. The area is filled with white to obtain the true value of the tidal flat surface, and the background is filled with black.

4. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: In step S3, a transfer learning method is used to fine-tune the land-sea segmentation deep learning model, which specifically includes: B1: Download the pre-trained weights of the HED-Unet land-sea segmentation deep learning model and freeze the shallow network of the feature extraction part of the model; B2: Adjust the loss function according to the characteristics of the tidal flat surface true value dataset in step S2. The adjusted loss function is: Among them, N represents the number of image samples input for each training, x n Represents the model output probability map, y n Represents the label information of the sample, ω n Indicates the ratio of the true value of the tidal flat surface to the background in the dataset; B3: Train the unfrozen feature extraction network layers and classification parts until the accuracy and loss function of the model basically converge.

5. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: The step S4 specifically includes: C1: Select a rectangular structure element, perform erosion and dilation operations on the binary image of the tidal flat obtained in step S3, and subtract the result after the erosion operation from the result after the dilation operation to obtain the contour line of the tidal flat; C2: Obtain the position of a pixel point on the contour line in a row scanning manner, take this point as the starting point, find the remaining pixel points in a clockwise or counterclockwise direction, and record the coordinate information; C3: Based on the coordinate information, the tidal flat elevation is obtained using the tidal flat elevation extraction method.

6. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: The step S6 specifically includes: D1: Construct a high-dimensional data set of hydrodynamic and meteorological data X = {x1, x2, x3, x4, x5, x6, x7, x8, x9}. The elements in the data set are: average wind speed at 10 meters above sea level, effective wave height, maximum tidal range, average tidal velocity, average sea surface temperature, average air temperature, cumulative precipitation, cumulative evaporation, and average sea level pressure; D2: Initialize target dimension: Y = {y1, y2, y3}; D3: Constructing a weighted graph: For each element x i and its nearest neighbor x j , the edge weight is calculated as: Among them, d(x i ,x j ) is x i and x j The Euclidean distance between i is x i The distance to its k nearest neighbors, σ i is a scale parameter that ensures that ∑ j w ij =log2(k ) ; D4: For a point y in a low-dimensional space i and j , define its similarity: z ij =(1+a·||y i -and j || 2b )- 1 , Among them, the values ​​of a and b are determined by the minimum distance; Use gradient descent to minimize the cross entropy loss function between high-dimensional and low-dimensional probability distributions: The final nonlinear structural feature Y is:

7. A method for predicting the spatiotemporal evolution of tidal flats according to claim 1, characterized in that: The CLN neural network model in step S7 includes: a convolutional layer module 1, a convolutional layer module 2, a LSTM+Dropout layer, a Dropout layer, and a fully connected layer; the convolutional layer module 1 and the convolutional layer module 2 have the same structure, both including: two convolutions with a convolution kernel size of 3, two ReLU activation functions, and a convolution with a convolution kernel size of 1; wherein the convolutional layer module 1 is the input port of the model; the convolutional layer module 2 is connected in series with the convolutional layer module 1 to form a multi-layer convolution stacking structure, which introduces more complex multi-level nonlinear features between sequence data into the model; wherein the LSTM+Dropout layer can pay more attention to the interaction relationship between short-term hydrodynamic and meteorological data and the interaction relationship between hydrodynamic data, while improving the robustness of the model when processing a large amount of data; the output results of the LSTM+Dropout layer and the convolutional layer module 2 are fused, so that the following LSTM layer can combine memories of different levels, and enhance the ability of the model to capture the evolution characteristics of tidal flats under the action of hydrodynamic and meteorological factors; The formula is as follows: Assume that the input of the model is tensor I, then: The output of convolutional layer module one is: I o1 =f 1×1 (Relu(f 3×3 (Relu(f 3×3 (I))))) Then after the calculation of the first layer of LSTM: h t (1) =LSTM1(x t ,h t-1 ) After the first layer of Dropout processing: The output of convolutional layer module 2 is: I o2 =f 1×1 (Relu(f 3×3 (Relu(f 3×3 (I o1 ))))) The input of the second layer LSTM undergoes feature fusion: The output of the second LSTM layer is: The input of the second layer of Dropout undergoes feature fusion: The output of the second layer of Dropout is: I o5 =I o4 ·m h After full connection calculation, the final output of the CLN model is: I CLN =f(W·I o5 +b) Among them, W is the weight matrix and b is the bias vector.

8. A method for predicting the spatiotemporal evolution of tidal flats according to claim 7, characterized in that: The neighborhood factor Ω in step S7 pq The calculation of is as follows: Among them, S pq It represents the proportion of various scouring and silting intensity levels in the m×m neighborhood.

9. A method for predicting the spatiotemporal evolution of tidal flats according to claim 8, characterized in that: The CLN neural network-cellular automaton model in step S8 is specifically expressed as: in, Indicates the current tidal flat erosion and siltation intensity status, Indicates the intensity state of tidal flat scouring and silting in the next period, which is represented by the tidal flat scouring and silting intensity level in step S5; CLN represents the state transition probability, which is calculated by the CLN neural network model; S is the random disturbance factor, which expresses the uncertainty of the state transition of the tidal flat scouring and silting intensity; S = 1 + (-lnβ) α , where β ranges from 0 to 1 and α ranges from 0 to 5.

10. A tidal flat spatiotemporal evolution prediction system, characterized in that: include: Data acquisition module, used to obtain tidal flat hydrodynamic and meteorological data, tidal flat remote sensing image data; The tidal flat information extraction module is used to train the land-sea segmentation model based on the collected tidal flat remote sensing images using the transfer learning method to obtain a binary image of the tidal flat surface that meets the accuracy requirements. After that, the tidal flat elevation is obtained using the morphological operator and the tidal flat elevation measurement method. The data processing module is used to calculate the average wind speed at 10 meters above the sea surface, the effective wave height, the maximum tidal range, the average tidal velocity, the average sea surface temperature, the average air temperature, the accumulated precipitation, the accumulated evaporation, and the average sea level pressure; and obtain the nonlinear structural characteristics between them based on the uniform manifold approximation and projection algorithm; Calculate the change in tidal flat elevation and define the scouring and silting intensity level of the tidal flat; The training module is used to input the output data of the data processing module into the pre-built CLN neural network model for training to obtain the optimal parameters; The prediction module is used to use the optimal parameters obtained from the training module as the conversion rules of the cellular automaton to predict the spatial changes of the tidal flat elevation in the next period.

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