Mangrove forest water level reduction evaluation method and mangrove forest water level reduction evaluation system based on hydrodynamic force and factorial design

Abstract

The invention relates to the technical field of hydrological analysis, and provides a mangrove forest water level reduction evaluation method and system based on hydrodynamic force and factorial design, and the method comprises the following steps: constructing a hydrodynamic model, and extracting the tide level and typhoon meteorological driving data of a target area; mangrove forest parameters are designed, mangrove forest data and tide level and typhoon weather driving data are input into the hydrodynamic model to drive the model, and the output water level is obtained; taking the output water level as a dependent variable to carry out two-factor factorial design test and analysis to obtain a factorial analysis result; and analyzing the factorial analysis result to obtain a storm water level reduction evaluation result under the mangrove forest influence. According to the method, a hydrodynamic numerical simulation method and a factorial test design method are combined, the main effect and the interaction effect of independent variables can be quantified, and the influence effect of different mangrove forest parameters in the storm period can be analyzed more comprehensively and perfectly.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological analysis, and more specifically, to a mangrove water level reduction assessment method and system based on hydrodynamics and factorial design. Background Art

[0002] Mangroves are distributed in strips along the coast, with luxuriant branches and leaves and a well-developed root system. They form a barrier that can defend against wind and waves, slow down water flow, promote siltation and protect the beach, and protect the coast. They are the first line of defense and an effective natural barrier for coastal areas to deal with typhoons. They play an important role in weakening the water level during coastal storms. Among them, mangroves and other parameter characteristics such as width and density will affect the reduction effect on the water level. For example, a mangrove with a width of 7 to 8 km can reduce the storm water level of Typhoon Wilma by about 80%; a mangrove with a width of one kilometer can reduce the storm water level by about 4 to 48 cm; the greater the density of mangroves, the greater the reduction in storm water level. Therefore, quantifying the impact of parameters such as mangrove width and density and their interactive effects on storm water levels is of great significance for optimizing mangrove protection and restoration projects.

[0003] At present, it is proposed to construct a bionic mangrove model, design and complete a physical model experiment, and then evaluate the water level reduction effect of mangroves based on the measurement results of the physical model experiment and analytical formulas. However, for the water level reduction effect of mangroves during storms, the existing single numerical simulation method cannot effectively quantify the main effect of the independent variable and the interaction effect between the two independent variables, and it is difficult to analyze the influence of different mangrove parameters during storms, so it has certain limitations. Summary of the invention

[0004] In order to overcome the defect of the above-mentioned prior art that the main effect of an independent variable and the interaction effect between two independent variables cannot be effectively quantified, the present invention provides a mangrove water level reduction assessment method and system based on hydrodynamics and factorial design.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A mangrove water level reduction assessment method based on hydrodynamics and factorial design includes the following steps:

[0007] Construct hydrodynamic models and extract tidal and typhoon meteorological driving data in the target area;

[0008] Design mangrove parameters, input the mangrove data and tide level and typhoon meteorological driving data into the hydrodynamic model to drive the model, and obtain an output water level;

[0009] Taking the output water level as the dependent variable, a two-factor factorial design experiment and analysis are performed to obtain factorial analysis results;

[0010] The factorial analysis results are analyzed to obtain the storm water level reduction assessment results under the influence of mangroves.

[0011] Furthermore, the present invention also proposes a mangrove water level reduction assessment system based on hydrodynamics and factorial design, and applies the mangrove water level reduction assessment method based on hydrodynamics and factorial design proposed by the present invention. The system includes:

[0012] Data acquisition module, used to extract tidal level and typhoon meteorological driving data in the target area;

[0013] A data processing module, on which a hydrodynamic model is mounted, for driving the hydrodynamic model according to input mangrove parameters and collected driving data to obtain an output water level;

[0014] The test analysis module is used to perform a two-factor factorial design test and analysis with the output water level as the dependent variable to obtain the factorial analysis results, and analyze the factorial analysis results to obtain the storm water level reduction assessment results under the influence of mangroves.

[0015] Furthermore, the present invention also proposes a storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the mangrove water level reduction assessment method based on hydrodynamics and factorial design as described in the present invention.

[0016] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0017] The present invention combines the hydrodynamic numerical simulation method with the factorial experimental design method, uses the output results of the hydrodynamic model as the input data for factorial analysis, takes different parameters of mangroves as independent variables, quantifies the main effect of the independent variables and the interaction effect between the two independent variables, and then analyzes and evaluates the impact of different mangrove parameters on storm water level reduction; compared with a single hydrodynamic numerical simulation method, the present invention can quantify the main effect and interaction effect of the independent variable, and can more comprehensively and completely analyze the impact of different mangrove parameters during the storm;

[0018] The present invention can determine suitable mangrove planting width, density and other parameters for the implementation of mangrove planting and protection projects, so as to optimize project benefits and save economic costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of a method for assessing mangrove water level reduction according to an embodiment of the present invention.

[0020] Figure 2 The figure is a schematic diagram of time-series water level data of a single sampling point during the typhoon Hato period according to an embodiment of the present invention.

[0021] Figure 3 A box plot comparing the differences in width and density of mangroves of different types according to an embodiment of the present invention.

[0022] Figure 4 FIG. 1 is an interaction diagram of mangrove width and density according to an embodiment of the present invention.

[0023] Figure 5 FIG. 1 is a response surface diagram of mangrove width and density according to an embodiment of the present invention.

[0024] Figure 6 FIG. 1 is a contour map showing the width and density of mangroves versus water level according to an embodiment of the present invention.

[0025] Figure 7 FIG. 1 is a Tukey mean difference diagram of the maximum water level according to an embodiment of the present invention.

[0026] Figure 8 It is an architecture diagram of a mangrove water level reduction assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0028] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0030] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Example 1

[0032] This embodiment proposes a mangrove water level reduction assessment method based on hydrodynamics and factorial design, such as Figure 1 , which is a flow chart of the mangrove water level reduction assessment method of this embodiment.

[0033] The mangrove water level reduction assessment method based on hydrodynamics and factorial design proposed in this embodiment includes the following steps:

[0034] S1. Build a hydrodynamic model and extract tidal and typhoon meteorological driving data in the target area;

[0035] S2. Design mangrove parameters, input the mangrove data and tide level and typhoon weather driving data into the hydrodynamic model to drive the model and obtain an output water level;

[0036] S3, taking the output water level as the dependent variable, conducting a two-factor factorial design experiment and analysis to obtain a factorial analysis result;

[0037] S4. Analyze the factor analysis results to obtain the storm water level reduction assessment results under the influence of mangroves.

[0038] In this embodiment, the hydrodynamic numerical simulation method and the factorial experimental design method are combined, the output results of the hydrodynamic model are used as the input data of the factorial analysis, and different parameters of mangroves are used as independent variables to quantify the main effect of the independent variable and the interaction effect between the two independent variables, and then analyze and evaluate the impact of different mangrove parameters on the reduction of storm water levels. Compared with a single hydrodynamic numerical simulation method, this embodiment can quantify the main effect and interaction effect of the independent variable, and can more comprehensively and completely analyze the impact of different mangrove parameters during the storm.

[0039] Among them, the two-factor factorial design experiment is an experimental design method that simultaneously examines the impact of two or more factors (independent variables) on an outcome variable. In this design experiment, different level combinations of each factor are tested to evaluate their independent effects on the results, that is, the main effects, and their interactions, that is, the interactions.

[0040] It should be noted that those skilled in the art can select different independent variables according to the research scenario and apply the mangrove water level reduction assessment method of this embodiment to perform analysis, which is not limited in this embodiment.

[0041] Exemplarily, this embodiment selects mangrove width and density as independent variable factors for analysis to evaluate the impact of different mangrove widths and densities on storm water level reduction.

[0042] In an optional embodiment, in step S1, constructing a hydrodynamic model and extracting tidal level and typhoon meteorological driving data of the target area include the following steps:

[0043] Construct an initial hydrodynamic model;

[0044] According to the shoreline data of the target area, the corresponding water depth data is obtained from the surface water simulation system, and the water depth data is interpolated using the natural neighbor method;

[0045] Importing the interpolated water depth data into the hydrodynamic model, and setting the model simulation time period, time step, bottom roughness, dry and wet water depth and / or stick-eddy coefficient;

[0046] Extract hourly tide boundary conditions from the global tide prediction model to obtain predicted tide driving data;

[0047] Extract wind speed and air pressure field data in directions U and V at preset height positions of the target area from the meteorological data set to obtain wind speed and air pressure field driving data;

[0048] The predicted tide level driving data and the wind speed and air pressure field driving data are introduced into the hydrodynamic model to drive the hydrodynamic model.

[0049] Exemplarily, the hydrodynamic model in this embodiment uses the MIKE 21 hydrodynamic model.

[0050] In an optional embodiment, the mangrove parameters in step S2 include mangrove width, height, breast diameter, density and drag coefficient; wherein the mangrove width parameter is converted into width codes Grid codes, and its expression is:

[0051]

[0052] Wherein, width is the width of the mangroves; a dfsu file is created according to the width encoding, and is used to set the mangrove coverage area in the hydrodynamic model.

[0053] In this embodiment, three widths of mangroves of 300m, 600m and 900m are optionally set from land to sea, and GridCodes correspond to 1, 2 and 3 respectively, and are further used to create a dfsu file. The dfsu file containing the width coding information is input into the hydrodynamic model to divide the mangrove coverage area.

[0054] In an optional embodiment, in step S3, the output water level is used as a dependent variable to perform a two-factor factorial design experiment and analysis, including the following steps:

[0055] S3.1. Read the maximum water level of each sampling point in the output water level, and build a regression model based on the ordinary least squares method to fit the two mangrove parameter factor independent variables and their interaction terms to obtain the factorial design test results; the expression is:

[0056] y=β0+β1x1+β2x2+ε

[0057] Among them, y is the dependent variable, that is, the output water level; x1 and x2 are independent variables, which are different mangrove parameter factors; β0, β1 and β2 are regression coefficients, and ε is the residual;

[0058] S3.2. Perform a factorial analysis on the regression model to obtain a factorial analysis result.

[0059] Exemplarily, in a specific implementation process, Python is used to read the water level output results of the hydrodynamic model, and create a factorial analysis input data table. The maximum water level of each sampling point in the output water level is added to the corresponding position of the factorial analysis input data table, and then ols in the Python third-party library is used to create an ordinary least squares regression model to fit two independent variables and their interaction terms.

[0060] Further, in an optional embodiment, in step S4, analyzing the factorial analysis result includes the following steps:

[0061] S4.1. Analyze the main effects and interaction effects of mangrove parameter factors and quantify the relative contribution rates;

[0062] S4.2. Draw a response surface diagram of different mangrove parameter factors and a contour diagram of water level of different mangrove parameter factors according to the regression model, and compare and quantify the difference of different mangrove parameter factors in reducing storm water level.

[0063] Furthermore, the expressions of the main effect and interaction effect of the mangrove parameter factors are:

[0064]

[0065]

[0066] Among them, A main_effect and B main_effect represents the main effect of factors A and B, and AB represents the interaction effect of factors AB; It represents the average response value of all B factor levels at the i-th level of factor A; It represents the average response value of all A factor levels at the i-th level of B factor; represents the average response value of the population; The average response value for factor A at the i level and factor B at the j level.

[0067] Furthermore, the analysis of the main effects and interaction effects of mangrove parameter factors and quantification of relative contribution rates includes the following steps:

[0068] Main and interaction effects were assessed using ANOVA, where the expression for the sum of squared deviations of the factors is:

[0069] SST=SSA+SSB+SSAB+SSE

[0070]

[0071] Among them, SST is the total variation, SSA and SSB are the main effect variation of factors A and B respectively, SSAB is the interaction effect variation of factors A and B, SSE is the error variation; I and J are the level numbers of factors A and B respectively, and K is the total number of repeated experiments; represents the average value of factor A at the i-th level, represents the average value of B factor at the jth level; represents the average value of factor A at level i and factor B at level j; Y ijk It indicates the kth value of factor A at the i-th level and factor B at the j-th level; is the overall mean;

[0072] Calculate the degree of freedom of the factor, the expression is:

[0073] df A =I-1

[0074] df B =J-1

[0075] df AB =(I-1)(J-1)

[0076] df E =IJ(K-1)

[0077] df total =IJK-1

[0078] Among them, df A and df B Represents the degrees of freedom of factor A and factor B, df AB Degrees of freedom for the interaction between factor A and factor B, df E Degrees of freedom for error, df total represents the total degrees of freedom;

[0079] Calculate the average sum of squares of the factors, the expression is:

[0080]

[0081] Among them, MSA and MSB represent the average sum of squares of factor A and factor B respectively, MSAB represents the average sum of squares of the interaction effect of factor A and factor B, and MSE represents the average sum of squares of error;

[0082] Calculate the F statistic of the factor, the expression is:

[0083]

[0084] Among them, F A and F B Represents the F statistics of factor A and factor B, F AB The F statistic represents the interaction effect between factor A and factor B. By comparing the F statistic of the factor with the critical value, it is determined whether factors A, B and the interaction AB have a significant impact on the dependent variable.

[0085] The relative contribution of factors is quantified based on their main effects and interaction effects; the expression is:

[0086]

[0087]

[0088] Among them, Contribution A and Contribution B Respectively represent the relative contribution rate of factor A and factor B; AB It represents the relative contribution rate of the interaction effect between factor A and factor B.

[0089] In this embodiment, the contribution of each factor and its interaction to the maximum water level variable is determined by decomposing the total variation, and the main effect and interaction effect are evaluated by combining the total sum of squares, degrees of freedom, and average sum of squares F statistics. The total sum of squares is used to evaluate the contribution of the factor to the total variation of the maximum water level of the corresponding variable. The larger the value, the greater the impact on the maximum water level. The average sum of squares is used to indicate the degree of variation of each degree of freedom. The F statistic is used to indicate the ratio of the factor variation to the residual variation. The larger the value, the greater the impact.

[0090] Furthermore, this embodiment draws response surface diagrams of different mangrove parameter factors and contour diagrams of different mangrove parameter factors versus water level based on the regression model, and compares and quantifies the differences in the effects of different mangrove parameter factors on storm water level reduction.

[0091] Taking the width and density of mangroves as parameter factors, the transition trend of the response value is obtained through the mangrove width and density response surface diagram, and the analysis shows that a smaller maximum water level response value can be obtained when the mangrove width and density are larger. Through the contour map of water level of different mangrove parameter factors, the effect of different width and density combinations on the maximum water level can be intuitively analyzed. In practical applications, the appropriate mangrove planting width and density can be determined for the implementation of mangrove planting and protection projects to optimize project benefits and save economic costs.

[0092] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0093] Draw a box plot of the difference comparison of different mangrove parameter factors and a mangrove parameter factor interaction diagram according to the regression model, and perform a visual display;

[0094] The Tukey post hoc test results between different parameter factors of mangroves were obtained through the Tukey test, and the mean difference diagram of different parameter factor groups of mangroves and the Tukey mean difference diagram of different parameter factor groups were drawn and visualized.

[0095] Among them, by visualizing the differences of different mangrove parameter factors through box plots and interaction plots, the differences of different mangrove parameter factors at different levels can be intuitively reflected. The Tukey test is a commonly used statistical method in the follow-up analysis of variance analysis. Its goal is to reveal significant differences between groups by comparing the differences between the means of each group.

[0096] In this embodiment, the F statistic is optionally compared with the critical value in the Tukey lookup table to determine whether factors A, B and interaction AB have a significant effect on the dependent variable. Further, the Tukey mean difference diagram is combined to display the mean difference of each combination, so that the significant difference of different parameter factor groups can be intuitively determined.

[0097] Example 2

[0098] This embodiment applies the mangrove water level reduction assessment method proposed in Example 1, proposes a specific application scenario, and implements it in combination with Python.

[0099] Specifically, this embodiment conducts hydrodynamic simulation and factorial experimental design on the morphological characteristics of Avicennia marina, the first dominant mangrove species in Shenzhen Bay, and the mangrove width and density parameters, and analyzes the storm water level under the influence of Avicennia marina mangroves.

[0100] Step 1: Construct a hydrodynamic model and extract tidal and typhoon meteorological driving data in the target area.

[0101] Among them, the MIEK 21 hydrodynamic model was selected, and data such as shoreline, water depth topography, and measured water level were collected for hydrodynamic modeling. Specifically, the shoreline in .kml format was obtained from Google Earth, and the googlekml2xyz tool was used to convert the shoreline data in kml format into a .cst format file that can be imported into the Surface Water Modeling System (SMS). In SMS, a good-quality unstructured triangular mesh was divided and saved as a .mesh file that can be used by the MIEK 21 hydrodynamic model.

[0102] The water depth data is obtained from the nautical chart, and the water depth data is interpolated using the Natural Neighbor method to create a mesh file containing the water depth.

[0103] Import the .mesh file containing water depth into the MIKE 21 hydrodynamic model, and set the model simulation time period, time step, bottom roughness, dry and wet water depth, vortex coefficient and other parameters. The simulation time period is set to the period of Typhoon Hato from 2017 / 8 / 20 00:00:00 to 2017 / 8 / 25 23:00:00, the time step is set to 60s, and the bottom roughness Manning number is set to The dry water depth was set to 0.005 m, the wet water depth was set to 0.1 m, and the vortex coefficient was set to 0.28.

[0104] In this embodiment, the tidal boundary conditions are driven by the predicted tidal level obtained by the global tidal prediction model of the MIKE 21 hydrodynamic model, and the typhoon is driven by the wind speed and pressure field data in the U and V directions at a height of 10 m obtained from the ERRA-5 reanalysis data in ECMWF. Furthermore, the calculated water level is compared with the measured water level collected by literature and other methods to calibrate the parameters and verify the hydrodynamic model, completing the construction of the hydrodynamic model.

[0105] Step 2: Design mangrove parameters, input the mangrove data and tide level and typhoon meteorological driving data into the hydrodynamic model to drive the model and obtain the output water level.

[0106] In this example, the width and density of mangroves are used as independent variable factors.

[0107] Specifically, different mangrove widths and densities are set in the hydrodynamic model. Taking Typhoon Hato as an example, the storm water level under different mangrove widths and densities is calculated by driving the model to obtain the output water level.

[0108] Among them, the mangrove width is set by creating a new dfsu file to set the mangrove coverage area, using width coding Grid Codes to identify mangrove areas of different widths, setting three widths of mangroves of 300m, 600m and 900m from land to sea, setting the model grid Grid Codes in the range of 0 to 300m to 1, the grid Grid Codes in the range of 300 to 600m to 2, and the grid Grid Codes in the range of 600 to 900m to 3. Then, the dfsu file containing the mangrove width coding information is loaded into the Material item in the MIKE 21 hydrodynamic model to set mangrove areas of different widths. Other mangrove parameters are set in the Vegetation item of the MIKE21 hydrodynamic model, including four items: mangrove height, breast diameter, density and drag coefficient. In this embodiment, the tree height of the Avicennia marina mangrove is set to 5.8m, the breast diameter is set to 16cm, the density is set to 4, and the drag coefficient is set to 16. Finally, check the Surface elevation water level output result in the Outputs item of the MIKE 21 hydrodynamic model to obtain the storm water level under different mangrove widths and densities.

[0109] Step 3: Conduct a two-factor factorial design experiment and analysis with the output water level as the dependent variable to obtain factorial analysis results.

[0110] The specific steps are as follows:

[0111] First, determine the width and density of the mangroves. In this embodiment, the factorial test design has two factors: mangrove width and density. The width is set at three levels of 300m, 600m and 900m, and the density is set at three levels of 1, 2, and 4, with a total of 3×3=9 working conditions, that is, each sampling point has 9 water level results.

[0112] Then, the Python third-party library Pandas is used to create the corresponding DataFrame factorial analysis input data table. The first column of the data table is Width, the second column is Density, and the third column is the surface elevation water level calculation result.

[0113] Secondly, all coastal nodes covered by mangroves were sampled. The third-party Python library mikeio.read function was used to extract the maximum water level results during Typhoon Hato in the output file .dfsu according to the X and Y coordinates of the coastal sampling points, and the maximum water level output results of each working condition were added to the corresponding position of the factorial analysis input data table. Figure 2 Shown is a schematic diagram of the time-series water level data of a single sampling point at different mangrove widths and densities during Typhoon Hato.

[0114] Furthermore, the Z-score normalized independent variables were used for the factorial analysis input data, and the data were adjusted to have a mean of 0 and a standard deviation of 1. The ordinary least squares regression model anova model was created using ols in the Python third-party statsmodels.formula.api library to fit the normalized data, and the factorial analysis was performed on the regression model anova_model using stats.anova_lm in the Python third-party library statsmodels.api to obtain the factorial analysis results.

[0115] Alternatively, the Python third-party library seaborn is used to draw a box plot comparing the width and density of different mangroves, such as Figure 3 As shown in the figure, the differences between different factors and different levels are visualized. Figure 3 The upper and lower ends of the box represent the upper and lower quartiles, respectively. The horizontal line inside the box represents the median. The upper and lower whiskers represent the maximum and minimum values, respectively. Figure 3 It is known that different mangrove width and density levels have different effects on the maximum water level along the coast. As the width or density of mangroves increases, the median, upper and lower quartiles, and maximum and minimum values ​​of the maximum water level along the coast gradually decrease. It can be seen that the increase in mangrove width and density can effectively reduce the maximum water level along the coast and thus improve coastal resilience.

[0116] Step 4: Analyze the factorial analysis results to obtain the storm water level reduction assessment results under the influence of mangroves.

[0117] The specific steps are as follows:

[0118] The stats.anova_lm in the third-party library statsmodels.api was used to perform factorial analysis on the regression model anova_model, where the parameter type=2 was set, indicating that the Type II method was used for analysis. This analysis type simultaneously considers the influence of all factors and the interaction between factors, and the factorial analysis table shown in Table 1 below is obtained, which shows the factorial analysis results of mangrove width and density.

[0119] Table 1 Factorial analysis of mangrove width and density

[0120]

[0121] The factorial analysis table in this embodiment determines the contribution of each factor and its interaction to the maximum water level variable by decomposing the total variation. Among them, df is the degree of freedom; sum_sq is the total sum of squares, which indicates the contribution of the factor to the total variation of the maximum water level of the corresponding variable. A larger sum_sq indicates a greater impact on the maximum water level; mean_sq is calculated by dividing the total sum of squares by the degree of freedom, indicating the degree of variation of each degree of freedom; the F value indicates the ratio of the variation of the factor to the variation of the residual. The larger the F value, the greater the impact; the P value indicates significance. A P value less than 0.05 indicates that the null hypothesis is rejected, that is, the factor is considered to have a significant impact on the maximum water level, and a P value greater than 0.05 indicates that the null hypothesis is accepted, that is, the impact is not significant.

[0122] As shown in Table 1, the main effects of mangrove width and density and their interaction effects are statistically significant on the maximum water level, and have significant effects on the maximum water level (P values ​​are all less than 0.01). The order of influence is width > density > interaction effect of width and density, and the contribution rates are 51.65%, 13.22% and 6.42% respectively. 2 and the adjusted R 2 It is known that the regression model can explain 71.3% of the variation of the dependent variable. The normalized regression equation is Surface_elevation = 2.0354-0.2118*Width-0.1072*Density-0.0747*Width*Density. The regression coefficients are significant (P<0.01) and negative, indicating that width, density and their interaction effects have a significant negative effect on the maximum water level. When the width and density are both 0, the maximum water level along the coast is 2.0354m; for every standard deviation increase in width (244.95m), the maximum water level decreases by 0.2118m; for every standard deviation increase in density (1.25 plants / m 2 ), the maximum water level decreased by 0.1072m; the interaction effect of width and density increased by one standard deviation (244.95m*1.25 plants / m 2 ), the maximum water level dropped by 0.0747m.

[0123] Alternatively, the interaction_plot function in the Python third-party library statsmodels.graphics.factorplots is used to draw an interaction diagram of mangrove width and density, as shown below: Figure 4 The interaction diagram of mangrove width and density is shown in the figure. The horizontal axis is the width, the vertical axis is the average maximum water level, and the solid line, dashed line and dash-dotted line are the interaction curves when the density is 1, 2 and 4 respectively. Figure 4It can be seen that at the same mangrove density, the average maximum water level decreases as the mangrove width increases; at the same mangrove width, the average maximum water level decreases as the mangrove density increases. The average maximum water level is the lowest when the mangrove width is 900m and the density is 4; when the mangrove width is 300m, the increase in density has little effect on the average maximum water level. As the mangrove width increases from 300m to 900m, the reduction in the average maximum water level increases, indicating that there is an interaction between width and density.

[0124] Further, optionally, based on the regression model anova_model, the 3D drawing function plot_surface in the Python third-party library matplotlib is used to draw the response surface diagram of different parameter factors of mangroves, and the contourf function in the Python third-party library matplotlib is used to draw the contour map of different parameter factors of mangroves to water level, and the following is obtained: Figure 5 , 6 The response surface plots of mangrove width and density and the contour plots of mangrove width and density versus water level are shown. Figure 5 It can be seen that the response surface gradually transitions from dark blue to light blue, showing that the maximum level of the response value changes from large to small, that is, a smaller maximum water level response value can be obtained when the mangrove forest has a larger width and a larger density. Figure 6 It can be seen that the contour lines can be obtained with Figure 5 Similar conclusions can also be drawn by visually analyzing the effects of different width and density combinations on the maximum water level. For example, the mangrove width is 700m and the density is 1 plant / m 2 With a width of 500m and a density of 2.6 plants / m 2 The combination of the above two conditions has the same impact on the maximum water level. The analysis of the results can determine the appropriate mangrove planting width and density for the implementation of mangrove planting and protection projects, so as to optimize the project benefits and save economic costs.

[0125] Further optionally, the pairwise_tukeyhsd function in the Python third-party library statsmodels.stats.multicomp is used to perform the Tukey test, and the Tukey post hoc test results between different mangrove widths, the Tukey post hoc test results between different mangrove densities, and the post hoc multiple comparison table between mangrove width and density are obtained as shown in Tables 2 to 4 below.

[0126] Table 2 Tukey post hoc test results between different mangrove widths

[0127]

[0128] Table 3 Results of Tukey post hoc test between different mangrove densities

[0129]

[0130] Table 4 Post hoc multiple comparisons between mangrove width and density

[0131]

[0132]

[0133]

[0134] Among them, group1 and group2 are the two groups to be compared; meandiff represents the mean difference between the two groups; p-adj is the adjusted p value, which is used to control multiple comparisons; lower and upper are the upper and lower limits of the 95% confidence interval of the mean difference; reject indicates whether to reject the null hypothesis, that is, whether there is a significant difference between the means of the two groups; and True means rejecting the null hypothesis, that is, there is a significant difference.

[0135] Furthermore, the errorbar function is used to draw the Tukey mean difference map of the maximum water level, such as Figure 7 As shown. Among them, Figure 7 (a) is the mean difference diagram of mangrove width groups. Figure 7 (b) is the mean difference map of mangrove density groups. Figure 7 (c) Tukey mean difference plot of different mangrove width and density groups.

[0136] From Table 2 and Figure 7 (a) It can be seen that the average maximum water level in the mangroves with a width of 600m is 0.1623m lower than that in the mangroves with a width of 300m (95%CI: 0.1216-0.203), P<0.05 is significant; the average maximum water level in the mangroves with a width of 900m is 0.5188m lower than that in the mangroves with a width of 300m (95%CI: 0.4782-0.5595), P<0.05 is significant; the average maximum water level in the mangroves with a width of 900m is 0.3565m lower than that in the mangroves with a width of 600m (95%CI: 0.3158-0.397), P<0.05 is significant. The maximum water level difference between mangroves with a width of 300m and 900m is the largest (0.5188m), followed by 600m and 900m (0.3565m), and finally 300m and 600m (0.1623m). It can be seen that there are significant differences in the reduction of maximum water level by mangroves of different widths. The larger the width, the greater the reduction in maximum water level.

[0137] Similarly, from Table 3 and Figure 7 (b) The differences between different mangrove densities can be analyzed, such as a density of 4 trees / m 2The average maximum water level density of mangroves is 1 tree / m 2 The mangrove forests at that time were 0.2639m lower (95%CI: 0.2081-0.3198), P<0.05, and the other analyses were similar. It can be seen that there are significant differences in the reduction of the maximum water level by mangroves of different densities. The greater the density, the greater the reduction of the maximum water level.

[0138] From Table 4 and Figure 7 (c) The mangrove forest is 300 m wide and has a density of 1 tree / m 2 With a width of 300m and a density of 2 plants / m 2 There was no significant difference in the effect of time on the maximum water level, and the rest of the analysis was similar.

[0139] The mangrove water level reduction assessment method designed in this embodiment can be implemented through different programming languages ​​​​such as Python, and has wide portability and applicability, so that it can be used in different systems and platforms.

[0140] Example 3

[0141] This embodiment applies the mangrove water level reduction assessment method proposed in Example 1, and proposes a mangrove water level reduction assessment system based on hydrodynamics and factorial design, such as Figure 8 , which is a structural diagram of the mangrove water level reduction assessment system of this embodiment.

[0142] The mangrove water level reduction assessment system proposed in this embodiment based on hydrodynamics and factorial design includes:

[0143] Data acquisition module, used to extract tidal level and typhoon meteorological driving data in the target area;

[0144] A data processing module, on which a hydrodynamic model is mounted, for driving the hydrodynamic model according to input mangrove parameters and collected driving data to obtain an output water level;

[0145] The test analysis module is used to perform a two-factor factorial design test and analysis with the output water level as the dependent variable to obtain the factorial analysis results, and analyze the factorial analysis results to obtain the storm water level reduction assessment results under the influence of mangroves.

[0146] It can be understood that the system of this embodiment corresponds to the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described repeatedly here.

[0147] Example 4

[0148] This embodiment proposes a storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the mangrove water level reduction assessment method proposed in Embodiment 1.

[0149] Exemplarily, the storage medium includes, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0150] Exemplarily, the instructions, programs, code sets or instruction sets may be implemented using conventional programming languages.

[0151] Exemplarily, the processor includes but is not limited to a smart phone, a personal computer, a server, a network device, etc., and is used to execute all or part of the steps of the mangrove water level reduction assessment method described in Example 1.

[0152] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely exemplary, in which the modules described as separate components may or may not be physically separated, and the functions of each module can be implemented in the same or one or more software and / or hardware when implementing the scheme of the present invention. It is also possible to select some or all of the modules according to actual needs to achieve the purpose of the scheme of this embodiment.

[0153] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A mangrove water level reduction assessment method based on hydrodynamics and factorial design, characterized in that: The following steps are involved: Construct hydrodynamic models and extract tidal and typhoon meteorological driving data in the target area; Design mangrove parameters, input the mangrove data and tide level and typhoon meteorological driving data into the hydrodynamic model to drive the model, and obtain an output water level; Taking the output water level as the dependent variable, a two-factor factorial design experiment and analysis are performed to obtain factorial analysis results; The factorial analysis results are analyzed to obtain the storm water level reduction assessment results under the influence of mangroves.

2. The method for assessing mangrove water level reduction according to claim 1, characterized in that: The construction of the hydrodynamic model and the extraction of tidal and typhoon meteorological driving data of the target area include the following steps: Construct an initial hydrodynamic model; According to the shoreline data of the target area, the corresponding water depth data is obtained from the surface water simulation system, and the water depth data is interpolated using the natural neighbor method; Importing the interpolated water depth data into the hydrodynamic model, and setting the model simulation time period, time step, bottom roughness, dry and wet water depth and / or stick-eddy coefficient; Extract hourly tide boundary conditions from the global tide prediction model to obtain predicted tide driving data; Extract wind speed and air pressure field data in directions U and V at preset height positions of the target area from the meteorological data set to obtain wind speed and air pressure field driving data; The predicted tide level driving data and the wind speed and air pressure field driving data are introduced into the hydrodynamic model to drive the hydrodynamic model.

3. The mangrove water level reduction assessment method according to claim 1, characterized in that: In the mangrove design parameters, the mangrove parameters include mangrove width, height, breast diameter, density and drag coefficient; Among them, the mangrove width parameter is converted into width code Grid Codes, and its expression is: Wherein, width is the width of the mangroves; a dfsu file is created according to the width encoding, and is used to set the mangrove coverage area in the hydrodynamic model.

4. The method for assessing mangrove water level reduction according to claim 1, characterized in that: A two-factor factorial design experiment and analysis is performed using the output water level as the dependent variable, including the following steps: The maximum water level of each sampling point in the output water level is read, and a regression model is constructed based on the ordinary least squares method to fit the two mangrove parameter factor independent variables and their interaction terms to obtain the factorial design test results; the expression is: y=β0+β1x1+β2x2+ε Among them, y is the dependent variable, that is, the output water level; x1 and x2 are independent variables, which are different mangrove parameter factors; β0, β1 and β2 are regression coefficients, and ε is the residual; A factor analysis is performed on the regression model to obtain a factor analysis result.

5. The method for assessing mangrove water level reduction according to claim 4, characterized in that: Analyzing the factorial analysis results includes the following steps: Analyze the main and interactive effects of mangrove parameter factors and quantify the relative contributions; According to the regression model, response surface diagrams of different mangrove parameter factors and contour diagrams of water level of different mangrove parameter factors are drawn to compare and quantify the differences of different mangrove parameter factors in reducing storm water levels.

6. The method for assessing mangrove water level reduction according to claim 5, characterized in that: The analysis of the main effects and interactive effects of mangrove parameter factors includes: Main and interaction effects were assessed using ANOVA, where the expression for the sum of squared deviations of the factors is: SST=SSA+SSB+SSAB+SSE Among them, SST is the total variation, SSA and SSB are the main effect variation of factors A and B respectively, SSAB is the interaction effect variation of factors A and B, SSE is the error variation; I and J are the level numbers of factors A and B respectively, and K is the total number of repeated experiments; represents the average value of factor A at the i-th level, represents the average value of B factor at the jth level; represents the average value of factor A at level i and factor B at level j; Y ijk It indicates the kth value of factor A at the i-th level and factor B at the j-th level; is the overall mean; Calculate the degree of freedom of the factor, the expression is: df A =I-1 df B =J-1 df AB =(I-1)(J-1) df E =IJ(K-1) df total =IJK-1 Among them, df A and df B Represents the degrees of freedom of factor A and factor B, df AB Degrees of freedom for the interaction between factor A and factor B, df E Degrees of freedom for error, df total represents the total degrees of freedom; Calculate the average sum of squares of the factors, the expression is: Among them, MSA and MSB represent the average sum of squares of factor A and factor B respectively, MSAB represents the average sum of squares of the interaction effect of factor A and factor B, and MSE represents the average sum of squares of error; Calculate the F statistic of the factor, the expression is: Among them, F A and F B Represents the F statistics of factor A and factor B, F AB The F statistic represents the interaction effect between factor A and factor B. By comparing the F statistic of the factor with the critical value, it is determined whether factors A, B and the interaction AB have a significant impact on the dependent variable. The relative contribution of factors is quantified based on their main effects and interaction effects; the expression is: Among them, Contribution A and Contribution B Respectively represent the relative contribution rate of factor A and factor B; AB It represents the relative contribution rate of the interaction effect between factor A and factor B.

7. The method for assessing mangrove water level reduction according to claim 6, characterized in that: The expressions of the main effect and interaction effect of the mangrove parameter factors are: Among them, A main_effect and B main_effect represents the main effect of factors A and B, and AB represents the interaction effect of factors AB; It represents the average response value of all B factor levels at the i-th level of factor A; It represents the average response value of all A factor levels at the i-th level of B factor; represents the average response value of the population; The average response value for factor A at the i level and factor B at the j level.

8. The method for assessing mangrove water level reduction according to claim 5, characterized in that: The method further comprises the following steps: Draw a box plot of the difference comparison of different mangrove parameter factors and a mangrove parameter factor interaction diagram according to the regression model, and perform a visual display; The Tukey post hoc test results between different parameter factors of mangroves were obtained through the Tukey test, and the mean difference diagram of different parameter factor groups of mangroves and the Tukey mean difference diagram of different parameter factor groups were drawn and visualized.

9. A mangrove water level reduction assessment system based on hydrodynamics and factorial design, using the mangrove water level reduction assessment method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, used to extract tidal level and typhoon meteorological driving data in the target area; A data processing module, on which a hydrodynamic model is mounted, for driving the hydrodynamic model according to input mangrove parameters and collected driving data to obtain an output water level; The test analysis module is used to perform a two-factor factorial design test and analysis with the output water level as the dependent variable to obtain the factorial analysis results, and analyze the factorial analysis results to obtain the storm water level reduction assessment results under the influence of mangroves.

10. A storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, all or part of the steps of the mangrove water level reduction assessment method based on hydrodynamics and factorial design as described in any one of claims 1 to 8 are implemented.

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