A method and system for predicting the spatial distribution of maximum wave height in three-dimensional island and reef terrain

Abstract

The present invention belongs to the field of ocean hydrodynamics technology and discloses a method and system for predicting the spatial distribution of maximum wave heights on three-dimensional island and reef terrain. The method comprises: calculating the average wave height at each measuring point based on wave surface changes at each measuring point on the island and reef terrain to obtain the spatial distribution of the maximum wave heights on the three-dimensional island and reef terrain; constructing a random forest model; and optimizing the random forest model based on the spatial distribution data of the maximum wave heights on the three-dimensional island and reef terrain; modifying the incident wave height, water depth, and wave direction data of the offshore waters according to the control variable method, and establishing a corresponding spatial distribution map of wave heights and a spatial distribution map of wave heights predicted by the random forest model; and comparing the aforementioned spatial distribution maps of wave heights to obtain data characteristics that have a greater impact on the spatial distribution of the maximum wave heights, thereby obtaining the spatial distribution of the maximum wave heights. The present invention achieves high-precision simulation and prediction of the propagation and evolution of waves on three-dimensional island and reef terrain, and has the advantages of high accuracy, strong flexibility, and broad application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean hydrodynamics and relates to a method and system for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain. Background Art

[0002] With the development of the economy and society and the need for resource development, the construction of buildings on islands and reefs is increasing. Rationally developing and utilizing marine resources and scientifically protecting the marine environment are crucial foundations for achieving my country's sustainable economic development strategy. Therefore, studying the wave propagation characteristics on islands and reefs, particularly the propagation characteristics of three-dimensional waves, is of great practical significance.

[0003] Currently, there has been considerable research on the propagation characteristics of waves on reef terrain under two-dimensional conditions. However, real-world waves are composed of waves of varying directions and frequencies. Therefore, research on the propagation characteristics of three-dimensional waves on reef terrain remains relatively limited. Existing research methods include physical model experiments and numerical simulations. Physical model experiments can intuitively simulate the propagation process of waves on reef terrain, but they are costly and time-consuming. Although numerical simulation methods are widely used in the study of three-dimensional wave propagation characteristics, the accuracy and reliability of the models still require continuous verification and optimization. In particular, for complex reef terrain and extreme wave conditions, the applicability and accuracy of the models require further research and improvement. Furthermore, forecasting methods for the spatial distribution of maximum wave heights on three-dimensional reef terrain are still imperfect; the lack of systematic forecasting methods and standards makes it difficult to ensure the accuracy and reliability of forecast results. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems that the accuracy and reliability of numerical simulation methods in the existing technology still need to be continuously verified and optimized, and the prediction method for the spatial distribution of maximum wave heights in three-dimensional island and reef terrain is still imperfect; there is a lack of systematic forecasting methods and standards, which makes it difficult to ensure the accuracy and reliability of the forecast results. A method and system for predicting the spatial distribution of maximum wave heights in three-dimensional island and reef terrain are provided.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for predicting the spatial distribution of maximum wave height in three-dimensional island and reef terrain, comprising:

[0007] Step 1: Extract the topographic characteristics of the three-dimensional island and reef terrain and collect wave data in the open sea;

[0008] Step 2: Based on the characteristics of offshore waves, a fully nonlinear wave numerical model is used to simulate the propagation and evolution of waves on three-dimensional island and reef terrain;

[0009] Step 3: Based on the wave surface changes at each measuring point of the island and reef terrain, the average wave height of each measuring point is calculated using the upper zero point method, and the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain is obtained;

[0010] Step 4: Based on the control variable method, the incident wave height, water depth, and wave direction of the open sea are changed respectively, and steps 2 to 3 are repeated based on the changed data, thereby establishing a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth, and wave direction of the open sea;

[0011] Step 5: Build a random forest model and optimize it based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain the optimal random forest model;

[0012] Step 6: Input the modified offshore incident wave height, water depth, and wave direction data into the random forest model to obtain a spatial distribution map of wave height predicted by the random forest model;

[0013] Step 7: Compare the wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyze the prediction effect and the error spatial distribution based on the relative error spatial distribution map, and obtain the data characteristics that have a greater impact on the spatial distribution of the maximum wave height, and then obtain the spatial distribution of the maximum wave height.

[0014] A further improvement of the present invention is:

[0015] Furthermore, wave data are collected offshore, including: several sets of water depths at different depths, the submerged water depth of the corresponding reef flats, the wave direction and incident wave height.

[0016] Furthermore, according to the wave surface changes at each measuring point of the island and reef terrain, the average wave height of each measuring point is calculated according to the upper crossing zero point method, and the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain is obtained, specifically: the wave surface data of each measuring point is preprocessed to remove outliers; and the position of the zero point crossing from positive to negative in the wave surface data is found; for each upper crossing zero point, the height from the previous wave trough to the next wave peak is calculated as the wave height; the wave heights of all measuring points are averaged to obtain the average wave height of the measuring point; if the number of measuring points is limited, the average wave heights of each measuring point are interpolated to obtain the spatial distribution of the average wave height of the entire island and reef terrain; in the spatial distribution obtained by interpolation, the maximum wave height value of each point is extracted to obtain the spatial distribution of the maximum wave height.

[0017] Furthermore, based on the control variable method, the incident wave height, water depth and wave direction of the open sea are changed respectively, specifically: the water depth and wave direction are controlled to remain unchanged, only the incident wave height is changed, and the changed incident wave height value does not appear in the wave data collected in the open sea; the wave direction and incident wave height are controlled to remain unchanged, only the water depth is changed, and the changed water depth value does not appear in the wave data collected in the open sea; the water depth and incident wave height are controlled to remain unchanged, only the wave direction is changed, and the changed wave direction value does not appear in the wave data collected in the open sea.

[0018] Furthermore, a wave height spatial distribution map was established based on numerical simulations of the changed offshore incident wave height, water depth, and wave direction, specifically:

[0019] The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the fully nonlinear wave numerical model. The simulated data are compared to obtain the maximum wave height value of each measuring point in each group of examples in the simulated data. The maximum wave height value is the maximum peak value, and a wave height spatial distribution map after the incident wave height data is changed is constructed;

[0020] Repeat the above steps and input the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; respectively construct the wave height spatial distribution map after the water depth data is changed and the wave height spatial distribution map after the wave direction data is changed.

[0021] Furthermore, the random forest model was optimized based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain the optimal random forest model, specifically:

[0022] The spatial distribution data of maximum wave heights in three-dimensional island and reef terrain were divided into training and test sets. An appropriate machine learning algorithm was selected as a benchmark model. Each candidate feature set in the training set was trained based on the benchmark model, and the performance of the benchmark model was evaluated. Based on the evaluation results, the feature set with the best performance was selected. On the selected feature set, the hyperparameters of the random forest model were optimized using grid search.

[0023] The training set is randomly divided into several subsets, and the random forest model is trained on the subsets using the five-fold cross-validation method; the performance of the model is evaluated based on the root mean square error (RMSE) as the objective function; the hyperparameter combination is continuously adjusted to obtain the optimal hyperparameter combination that minimizes the RMSE;

[0024] Substitute the samples in the test set into the trained random forest model, and test the accuracy of the model by comparing the predicted results with the actual values ​​in the test set; based on the test results, optimize and adjust the random forest model until the random forest model with the best performance is found.

[0025] Furthermore, the modified offshore incident wave height, water depth, and wave direction data were input into the optimal random forest model to obtain the spatial distribution map of wave height predicted by the random forest model, specifically:

[0026] The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the optimal random forest model to obtain the spatial distribution map of wave height predicted by the random forest model of the changed incident wave height data;

[0027] Repeat the above steps, input the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; respectively construct the spatial distribution map of wave height predicted by the random forest model after the water depth data is changed and the spatial distribution map of wave height predicted by the random forest model after the wave direction data is changed.

[0028] Furthermore, the wave height spatial distribution map drawn by the numerical simulation of the modified offshore incident wave height, water depth and wave direction is compared with the wave height spatial distribution map predicted by the random forest model. Based on the relative error spatial distribution map, the prediction effect and the error spatial distribution are analyzed. The data characteristics that have a greater impact on the spatial distribution of the maximum wave height are obtained, and then the spatial distribution of the maximum wave height is obtained, which is specifically:

[0029] The wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction is compared with the wave height spatial distribution map predicted by the random forest model to determine whether the relative error of the wave height exceeds the preset threshold. If not, it is determined that the offshore wave data corresponding to the wave height relative error closest to the threshold has the greatest influence on the spatial distribution of the maximum wave height, and the wave height spatial distribution predicted by the random forest model of the offshore wave data is the maximum wave height spatial distribution; if it exceeds, the random forest model is continuously optimized until the maximum wave height spatial distribution is obtained.

[0030] A three-dimensional island and reef terrain maximum wave height spatial distribution prediction system, comprising:

[0031] An acquisition module, which extracts the topographic characteristics of the three-dimensional island and reef terrain and collects wave data in the open sea;

[0032] A simulation module, which simulates the propagation and evolution of waves on three-dimensional island and reef terrain based on the characteristics of offshore waves and a fully nonlinear wave numerical model;

[0033] A calculation module, which calculates the average wave height of each measuring point according to the wave surface changes of each measuring point of the island and reef terrain using the upper crossing zero point method, and obtains the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain;

[0034] A construction module, wherein the construction module changes the incident wave height, water depth and wave direction of the open sea respectively based on a control variable method, and establishes a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth and wave direction of the open sea;

[0035] An optimization module, wherein the optimization module constructs a random forest model and optimizes the random forest model based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain an optimal random forest model;

[0036] an acquisition module, wherein the acquisition module inputs the modified offshore incident wave height, water depth, and wave direction data into a random forest model to obtain a spatial distribution map of wave height predicted by the random forest model;

[0037] A comparison module compares the wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyzes the prediction effect and the error spatial distribution based on the relative error spatial distribution map, obtains the data characteristics that have a greater impact on the maximum wave height spatial distribution, and then obtains the maximum wave height spatial distribution.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention simulates the propagation and evolution of waves in three-dimensional island and reef terrain through offshore wave data and a fully nonlinear wave numerical model, and optimizes the constructed random forest model through the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain; according to the control variable method, the offshore incident wave height, water depth and wave direction data are changed respectively, and a wave height spatial distribution map drawn by numerical simulation of the changed offshore incident wave height, water depth and wave direction is established, and a wave height spatial distribution map predicted by the random forest model is obtained; and the above-mentioned wave height spatial distribution maps are compared, and the prediction effect and error spatial distribution are analyzed based on the relative error spatial distribution map, and the data characteristics that have a greater impact on the spatial distribution of the maximum wave height are obtained, and then the spatial distribution of the maximum wave height is obtained. The present invention realizes high-precision simulation and prediction of the propagation and evolution of waves on three-dimensional island and reef terrain by integrating numerical simulation technology and random forest models, and has the advantages of high precision, flexibility, high efficiency, data-driven optimization and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1Schematic diagram of the flow of the method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain of the present invention;

[0042] Figure 2 Schematic diagram of the structure of the three-dimensional island and reef terrain maximum wave height spatial distribution prediction system of the present invention;

[0043] Figure 3 This is a schematic diagram of the topographic structure of three-dimensional islands and reefs;

[0044] Figure 4 The data diagram of each measuring point in the example is shown in Figure 2.

[0045] Figure 5 Schematic diagram for building a random forest model;

[0046] Figure 6 This is a schematic diagram comparing the hyperparameters of the random forest model before and after tuning;

[0047] Figure 7 is the spatial distribution of wave height of numerical simulation results;

[0048] Figure 8 Spatial distribution map of wave height based on random forest prediction results;

[0049] Figure 9 This is the spatial distribution map of wave height of numerical simulation results and the spatial distribution map of relative errors of wave height based on random forest prediction results. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0051] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0052] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0053] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0054] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0055] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0056] The present invention is described in further detail below with reference to the accompanying drawings:

[0057] See also Figure 1 The present invention discloses a method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain, comprising:

[0058] S101, extracting the topographic characteristics of the three-dimensional island and reef terrain and collecting wave data in the open sea;

[0059] Collect wave data in the open sea, including: several sets of water depths at different depths, the submerged water depth of the corresponding reef flat, the wave direction and incident wave height.

[0060] S102, based on the characteristics of offshore waves, simulates the propagation and evolution of waves on three-dimensional island and reef terrain using a fully nonlinear wave numerical model;

[0061] S103, based on the wave surface changes at each measuring point of the island and reef terrain, the average wave height of each measuring point is calculated using the upper crossing zero point method, and the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain is obtained;

[0062] The wave surface data of each measuring point are preprocessed to remove outliers; the position of the zero crossing from positive to negative in the wave surface data is found; for each zero crossing point, the height from the previous wave trough to the next wave peak is calculated as the wave height; the wave heights of all measuring points are averaged to obtain the average wave height of the measuring point; if the number of measuring points is limited, the average wave heights of each measuring point are interpolated to obtain the average wave height spatial distribution of the entire island and reef terrain; in the spatial distribution obtained by interpolation, the maximum wave height value of each point is extracted to obtain the maximum wave height spatial distribution.

[0063] S104, based on the control variable method, respectively changing the incident wave height, water depth and wave direction of the open sea, and repeating S102 to S103 based on the changed data, thereby establishing a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth and wave direction of the open sea;

[0064] Based on the control variable method, the incident wave height, water depth and wave direction of the open sea are changed respectively, specifically: the water depth and wave direction are kept unchanged, only the incident wave height is changed, and the changed incident wave height value does not appear in the wave data collected in the open sea; the wave direction and incident wave height are kept unchanged, only the water depth is changed, and the changed water depth value does not appear in the wave data collected in the open sea; the water depth and incident wave height are kept unchanged, only the wave direction is changed, and the changed wave direction value does not appear in the wave data collected in the open sea.

[0065] A wave height spatial distribution map was established based on numerical simulations of the changed offshore incident wave height, water depth, and wave direction, specifically:

[0066] The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the fully nonlinear wave numerical model. The simulated data are compared to obtain the maximum wave height value of each measuring point in each group of examples in the simulated data. The maximum wave height value is the maximum peak value, and a wave height spatial distribution map after the incident wave height data is changed is constructed;

[0067] Repeat the above steps and input the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; respectively construct the wave height spatial distribution map after the water depth data is changed and the wave height spatial distribution map after the wave direction data is changed.

[0068] S105, constructing a random forest model and optimizing the random forest model based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain the optimal random forest model;

[0069] The spatial distribution data of maximum wave heights in three-dimensional island and reef terrain were divided into training and test sets. An appropriate machine learning algorithm was selected as a benchmark model. Each candidate feature set in the training set was trained based on the benchmark model, and the performance of the benchmark model was evaluated. Based on the evaluation results, the feature set with the best performance was selected. On the selected feature set, the hyperparameters of the random forest model were optimized using grid search.

[0070] The training set is randomly divided into several subsets, and the random forest model is trained on the subsets using the five-fold cross-validation method; the performance of the model is evaluated based on the root mean square error (RMSE) as the objective function; the hyperparameter combination is continuously adjusted to obtain the optimal hyperparameter combination that minimizes the RMSE;

[0071] Substitute the samples in the test set into the trained random forest model, and test the accuracy of the model by comparing the predicted results with the actual values ​​in the test set; based on the test results, optimize and adjust the random forest model until the random forest model with the best performance is found.

[0072] S106, inputting the modified offshore incident wave height, water depth, and wave direction data into a random forest model to obtain a spatial distribution map of wave height predicted by the random forest model;

[0073] The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the optimal random forest model to obtain the spatial distribution map of wave height predicted by the random forest model of the changed incident wave height data;

[0074] Repeat the above steps, input the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; respectively construct the spatial distribution map of wave height predicted by the random forest model after the water depth data is changed and the spatial distribution map of wave height predicted by the random forest model after the wave direction data is changed.

[0075] S107, compare the wave height spatial distribution map drawn by the numerical simulation of the modified offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyze the prediction effect and the error spatial distribution based on the relative error spatial distribution map, and obtain the data characteristics that have a greater impact on the spatial distribution of the maximum wave height, and then obtain the spatial distribution of the maximum wave height.

[0076] The wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction is compared with the wave height spatial distribution map predicted by the random forest model to determine whether the relative error of the wave height exceeds the preset threshold. If not, it is determined that the offshore wave data corresponding to the wave height relative error closest to the threshold has the greatest influence on the spatial distribution of the maximum wave height, and the wave height spatial distribution predicted by the random forest model of the offshore wave data is the maximum wave height spatial distribution; if it exceeds, the random forest model is continuously optimized until the maximum wave height spatial distribution is obtained.

[0077] See also Figure 2 The present invention discloses a three-dimensional island and reef terrain maximum wave height spatial distribution prediction system, comprising:

[0078] An acquisition module, which extracts the topographic characteristics of the three-dimensional island and reef terrain and collects wave data in the open sea;

[0079] A simulation module, which simulates the propagation and evolution of waves on three-dimensional island and reef terrain based on the characteristics of offshore waves and a fully nonlinear wave numerical model;

[0080] A calculation module, which calculates the average wave height of each measuring point according to the wave surface changes of each measuring point of the island and reef terrain using the upper crossing zero point method, and obtains the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain;

[0081] A construction module, wherein the construction module changes the incident wave height, water depth and wave direction of the open sea respectively based on a control variable method, and establishes a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth and wave direction of the open sea;

[0082] An optimization module, wherein the optimization module constructs a random forest model and optimizes the random forest model based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain an optimal random forest model;

[0083] an acquisition module, wherein the acquisition module inputs the modified offshore incident wave height, water depth, and wave direction data into a random forest model to obtain a spatial distribution map of wave height predicted by the random forest model;

[0084] A comparison module compares the wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyzes the prediction effect and the error spatial distribution based on the relative error spatial distribution map, obtains the data characteristics that have a greater impact on the maximum wave height spatial distribution, and then obtains the maximum wave height spatial distribution.

[0085] Example: The present invention discloses a method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain, comprising the following steps:

[0086] Step 1: Determine the measured island and reef topography data, collect offshore wave data, and perform numerical simulation on the offshore wave data:

[0087] Simulate the evolution of wave propagation on island and reef terrain and arrange measuring points. In order to obtain the wave height distribution along the path, a total of 56 measuring points were arranged. The numerical simulation conditions used to construct the training set included water depths h of 0.30m, 0.325m, 0.35m, 0.375m, and 0.40m, corresponding to the submerged water depth h of the reef flat. r The water depths are 0m, 0.025m, 0.05m, 0.075m, and 0.1m, respectively. The wave directions θ are 0°, 22.5°, 45°, 67.5°, and 90°, respectively. The incident wave heights are 0.04m, 0.05m, 0.06m, 0.07m, and 0.08m, respectively. The incident wave type is random. The simulations were conducted for 5 × 5 × 5 = 125 combinations of different water depths, wave directions, and wave heights. A large number of calculation examples were performed using the verified fully nonlinear wave numerical model. Each example outputs the water level changes at 56 measuring points at a frequency of 50 Hz, providing training data for the random forest.

[0088] Step 2: Construct random forest training set:

[0089] The results of the 125 simulation cases were processed and analyzed to obtain the maximum wave height value of each measuring point in each case. Finally, the training data set was obtained, as shown in the following figure: Figure 4 The features are: offshore water depth h, wave direction θ, incident wave height H0, measuring point number, and the corresponding label is the maximum wave height.

[0090] Step 3: Establishment of the maximum wave height prediction model based on random forest:

[0091] Construct a random forest algorithm, such as Figure 5 , 1) Data preparation. All the data in this study are provided by numerical simulation. As mentioned above, in the same data set, 80% of the samples are used for training and 20% of the samples are used for testing. 2) Hyperparameter optimization. First, the benchmark model is used for preliminary screening to determine the optimal combination of input variables; then, on the training set of the selected feature set, grid search (Grid Search CV) is used to optimize the hyperparameters of the machine learning algorithm. 3) Model construction. During the training process, the training data is randomly divided and the 5-fold cross-validation method is used. At the same time, the root mean square error is used as the objective function to find the optimal hyperparameter combination. 4) Model evaluation. The data samples in the test set are used for prediction evaluation. The samples in the test set are substituted into the random forest model after training to make predictions and test its accuracy.

[0092] Note: For the hyperparameter optimization part, according to the actual situation of this study, the number of decision trees (n-estimators), the maximum depth of each decision tree (max-depth), the minimum number of separation samples (min-samples-split) and the minimum number of leaf node samples (min-samples-leaf) were selected for hyperparameter optimization. Figure 6 shown.

[0093] Step 6: Optimize the prediction results:

[0094] Evaluate the predictive performance of the prediction model established in step 3. Machine learning models are typically evaluated using a test set, a 20% sample set from the training set. Models are then evaluated using metrics such as the coefficient of determination (R²), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). Based on the specific circumstances of this study, this section not only utilizes common evaluation methods but also proposes a novel approach. It includes four categories: water depth, wave direction, incident wave height, and measuring point. The prediction effect of the random forest model is evaluated by the control variable method. Since the measuring point variable is a discrete variable, it only represents the various locations of the islands and reefs. Each set of examples contains measuring points 1 to 56 and is fixed. We regard the islands and reefs as a whole and evaluate the model from the following three perspectives: First, control the water depth and wave direction and only change the incident wave height, and the changed incident wave height value does not appear in the wave data collected in the offshore area; second, control the wave direction and incident wave height and only change the water depth, and the changed water depth value does not appear in the wave data collected in the offshore area; third, control the water depth and incident wave height and only change the wave direction, and the changed wave direction value does not appear in the wave data collected in the offshore area. Then, these three types of data are input into the fully nonlinear wave numerical model respectively, and three types of numerical simulations are used to draw the spatial distribution maps of wave height. The three types of data are input into the optimal random forest model respectively, and three types of wave height spatial distribution maps predicted by the random forest model are obtained;

[0095] The wave height spatial distribution maps drawn from these three types of numerical simulations were compared with the three types of wave height spatial distribution maps predicted by the random forest model to determine which data type (water depth, wave direction, or incident wave height) has the greatest impact on the predicted maximum wave height spatial distribution. The spatial distribution of wave height predicted by the random forest model using the wave data corresponding to the data type with the greatest impact on the maximum wave height spatial distribution was then used as the maximum wave height spatial distribution.

[0096] In this embodiment, a set of working conditions AS36055-34 is randomly selected, that is, the input offshore water depth is 0.36m, the input wave direction is 34°, the input wave height is 0.055m, and the input measuring points 1 to 56 are input to obtain the maximum wave height. The wave height spatial distribution diagram based on the numerical simulation results is drawn as shown in FIG. Figure 7 The spatial distribution of wave height based on the random forest prediction results is shown in the figure below: Figure 8 and two The relative error space distribution diagram of Figure 9 The average error is only about 5%. This method can effectively predict the spatial distribution of maximum wave heights over three-dimensional island and reef terrain. Timely prediction of maximum wave heights over island and reef terrain can effectively prevent significant damage to life and structures.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting the spatial distribution of maximum wave height in three-dimensional island and reef terrain, characterized by: include: Step 1: Extract the topographic characteristics of the three-dimensional island and reef terrain and collect wave data in the open sea; Step 2: Based on the characteristics of offshore waves, a fully nonlinear wave numerical model is used to simulate the propagation and evolution of waves on three-dimensional island and reef terrain; Step 3: Based on the wave surface changes at each measuring point of the island and reef terrain, the average wave height of each measuring point is calculated using the upper zero point method, and the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain is obtained; Step 4: Based on the control variable method, the incident wave height, water depth, and wave direction of the open sea are changed respectively, and steps 2 to 3 are repeated based on the changed data, thereby establishing a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth, and wave direction of the open sea; Step 5: Build a random forest model and optimize it based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain the optimal random forest model; Step 6: Input the modified offshore incident wave height, water depth, and wave direction data into the random forest model to obtain a spatial distribution map of wave height predicted by the random forest model; Step 7: Compare the wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyze the prediction effect and the error spatial distribution based on the relative error spatial distribution map, and obtain the data characteristics that have a greater impact on the spatial distribution of the maximum wave height, and then obtain the spatial distribution of the maximum wave height.

2. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 1 is characterized in that: The wave data collected at the offshore location include: several groups of water depths at different depths, the submerged water depths of the corresponding reef flats, the wave directions and incident wave heights.

3. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 2 is characterized in that: The method of calculating the average wave height of each measuring point according to the wave surface changes of each measuring point on the island and reef terrain using the upper zero point method and obtaining the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain is specifically as follows: pre-processing the wave surface data of each measuring point to remove outliers; And find the position of the zero point crossing from positive to negative in the wave surface data; for each zero crossing point, calculate the height from the previous wave trough to the next wave crest as the wave height; average the wave heights of all measuring points to obtain the average wave height of the measuring point; If the number of measuring points is limited, the average wave height of each measuring point is interpolated to obtain the spatial distribution of the average wave height of the entire island and reef terrain; in the spatial distribution obtained by interpolation, the maximum wave height value of each point is extracted to obtain the spatial distribution of the maximum wave height.

4. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 3 is characterized in that: The control variable method is based on respectively changing the incident wave height, water depth and wave direction of the open sea, specifically: controlling the water depth and wave direction to remain unchanged, only changing the incident wave height, and the changed incident wave height value does not appear in the wave data collected in the open sea; controlling the wave direction and incident wave height to remain unchanged, only changing the water depth, and the changed water depth value does not appear in the wave data collected in the open sea; controlling the water depth and incident wave height to remain unchanged, only changing the wave direction, and the changed wave direction value does not appear in the wave data collected in the open sea.

5. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 4 is characterized in that: The wave height spatial distribution map drawn by numerical simulation including the modified offshore incident wave height, water depth and wave direction is specifically as follows: The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the fully nonlinear wave numerical model. The simulated data are compared to obtain the maximum wave height value of each measuring point in each group of examples in the simulated data. The maximum wave height value is the maximum peak value, and a wave height spatial distribution map after the incident wave height data is changed is constructed; Repeat the above steps, inputting the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; The spatial distribution maps of wave height after changing the water depth data and the spatial distribution maps of wave height after changing the wave direction data are constructed respectively.

6. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 5 is characterized in that: The random forest model is optimized based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain the optimal random forest model, specifically: The spatial distribution data of maximum wave heights in three-dimensional island and reef terrain are divided into training and test sets. An appropriate machine learning algorithm is selected as a benchmark model. Each candidate feature set in the training set is trained based on the benchmark model, and the performance of the benchmark model is evaluated. Based on the evaluation results, the feature set with the best performance is selected. Optimize the hyperparameters of the random forest model based on the selected feature set using grid search; The training set is randomly divided into several subsets, and the random forest model is trained on the subsets using the five-fold cross-validation method; the performance of the model is evaluated based on the root mean square error (RMSE) as the objective function; the hyperparameter combination is continuously adjusted to obtain the optimal hyperparameter combination that minimizes the RMSE; Substitute the samples in the test set into the trained random forest model, and test the accuracy of the model by comparing the predicted results with the actual values ​​in the test set; based on the test results, optimize and adjust the random forest model until the random forest model with the best performance is found.

7. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 6 is characterized in that: The modified offshore incident wave height, water depth, and wave direction data are input into the optimal random forest model to obtain a spatial distribution map of wave height predicted by the random forest model, specifically: The water depth and wave direction remain unchanged, and the changed incident wave height data is input into the optimal random forest model to obtain the spatial distribution map of wave height predicted by the random forest model of the changed incident wave height data; Repeat the above steps, input the changed water depth data with the wave direction and incident wave height unchanged and the changed wave direction data with the water depth and incident wave height unchanged into the fully nonlinear wave numerical model; respectively construct the spatial distribution map of wave height predicted by the random forest model after the water depth data is changed and the spatial distribution map of wave height predicted by the random forest model after the wave direction data is changed.

8. The method for predicting the spatial distribution of maximum wave height of three-dimensional island and reef terrain according to claim 7 is characterized in that: The wave height spatial distribution map drawn by the numerical simulation of the modified offshore incident wave height, water depth and wave direction and the wave height spatial distribution map predicted by the random forest model are compared. The prediction effect and the error spatial distribution are analyzed based on the relative error spatial distribution map, and the data characteristics that have a greater impact on the spatial distribution of the maximum wave height are obtained, and then the maximum wave height spatial distribution is obtained, which is specifically: The wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction is compared with the wave height spatial distribution map predicted by the random forest model to determine whether the relative error of the wave height exceeds the preset threshold. If not, it is determined that the offshore wave data corresponding to the wave height relative error closest to the threshold has the greatest influence on the spatial distribution of the maximum wave height, and the wave height spatial distribution predicted by the random forest model of the offshore wave data is the maximum wave height spatial distribution; if it exceeds, the random forest model is continuously optimized until the maximum wave height spatial distribution is obtained.

9. A three-dimensional island and reef terrain maximum wave height spatial distribution prediction system, characterized by: include: An acquisition module, which extracts the topographic characteristics of the three-dimensional island and reef terrain and collects wave data in the open sea; A simulation module, which simulates the propagation and evolution of waves on three-dimensional island and reef terrain based on the characteristics of offshore waves and a fully nonlinear wave numerical model; A calculation module, which calculates the average wave height of each measuring point according to the wave surface changes of each measuring point of the island and reef terrain using the upper crossing zero point method, and obtains the spatial distribution of the maximum wave height of the three-dimensional island and reef terrain; A construction module, wherein the construction module changes the incident wave height, water depth and wave direction of the open sea respectively based on a control variable method, and establishes a wave height spatial distribution map drawn by numerical simulation of the changed incident wave height, water depth and wave direction of the open sea; An optimization module, wherein the optimization module constructs a random forest model and optimizes the random forest model based on the spatial distribution data of the maximum wave height of the three-dimensional island and reef terrain to obtain an optimal random forest model; an acquisition module, wherein the acquisition module inputs the modified offshore incident wave height, water depth, and wave direction data into a random forest model to obtain a spatial distribution map of wave height predicted by the random forest model; A comparison module compares the wave height spatial distribution map drawn by the numerical simulation of the changed offshore incident wave height, water depth and wave direction with the wave height spatial distribution map predicted by the random forest model, analyzes the prediction effect and the error spatial distribution based on the relative error spatial distribution map, obtains the data characteristics that have a greater impact on the maximum wave height spatial distribution, and then obtains the maximum wave height spatial distribution.

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