A method and system for predicting lake wetland plant types by integrating two-dimensional hydrodynamic model and CA-Markov

Abstract

The present invention discloses a method and system for predicting plant types in lake wetlands that integrates a two-dimensional hydrodynamic model and CA-Markov. The method first simulates hydrological and hydrodynamic parameters by establishing a two-dimensional hydrodynamic model of lake wetlands, and calculates relevant hydrological situation indicators that affect lake wetland plants; then, a Gaussian function is used to fit the relationship between hydrological situation indicators and different plant types, and the hydrological situation ecological thresholds of different plant types are obtained; finally, according to the hydrological situation and plant types of the current period, a cellular automaton-Markov method is used to establish a transfer matrix of plant types in the next period, and a fuzzy set membership function is used to make a hydrological situation ecological threshold suitability map, and as a constraint condition of the transfer matrix, the plant type at the next moment is finally obtained. The present invention can provide important support for accurate prediction of wetland plants.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy technology, and in particular to a lake wetland plant type prediction method and system integrating a two-dimensional hydrodynamic model and CA-Markov. Background Art

[0002] The hydrological regime is the main driving force for the development and evolution of wetland ecosystems, and mainly refers to the changes in hydrological and hydraulic elements in time and space. Water conservancy project construction and climate change can directly or indirectly change the hydrological regime of lake wetlands, leading to large-scale losses of natural wetland vegetation, causing the loss of wetland ecological functions and the decline of biodiversity, and then leading to a continuous decrease in the species and number of wetland protected animals. Carrying out simulation and prediction of lake wetland plant types is the basis of plant protection and is of great significance to the maintenance of wetland biodiversity.

[0003] Cellular Automata-Markov Chain (CA-Markov) is a process-based dynamic system method that can well explain the spatial effects of plant population dynamics and simulate continuous environmental dynamic processes and the spatiotemporal evolution of wetland plants. It has been widely used to simulate plant growth and spread. Current studies mostly use remote sensing to interpret the dynamics of wetland plant communities, thereby obtaining plant type distribution, and then use the CA-Markov method to simulate plant succession. However, such studies ignore the important impact of hydrological processes on plant succession, and insufficiently consider the phenological differences between plants caused by changes in wetland hydrological conditions. Summary of the invention

[0004] The present invention provides a lake wetland plant type prediction method and system integrating a two-dimensional hydrodynamic model and CA-Markov, so as to solve the problem that the prior art ignores the important influence of hydrological process on plant succession and does not give enough consideration to the phenological differences among plants caused by changes in wetland hydrological regime.

[0005] In a first aspect, the present invention provides a method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov, comprising:

[0006] Step 1: Obtain basic data related to lake wetland hydrology and meteorology, wherein the basic data includes lake topography data, daily-scale hydrological data, daily-scale meteorological data, and plant type data of the lake wetland;

[0007] Step 2: Based on the basic data, the upper and lower boundaries of the study area are set, the roughness parameters of the model are defined, and a two-dimensional hydrodynamic model is constructed using the two-dimensional incompressible Navier-Stokes equations.

[0008] Step 3, using the annual flooding time as a hydrological indicator, and calculating the annual flooding time according to the water depth data of each hydrodynamic grid of the lake wetland obtained by the two-dimensional hydrodynamic model;

[0009] Step 4: Use Gaussian function to fit the relationship between hydrological indicators and different plant types;

[0010] Step 5: Calculate the ecological threshold of hydrological conditions of different plant types based on the fitting results of the Gaussian function;

[0011] Step 6: Based on the plant types of the current year and the annual flooding time, the cellular automaton-Markov method is used to establish the transition matrix of the plant types in the next period;

[0012] Step seven, use the fuzzy set membership function to make the hydrological situation ecological threshold suitability map, and use it as the constraint condition of the transfer matrix to finally obtain the plant type at the next moment.

[0013] Furthermore, in step one, the daily-scale hydrological data includes daily-scale flow data and water level data; the daily-scale meteorological data includes daily-scale rainfall data, temperature data, and wind speed data.

[0014] Furthermore, in step 2, the continuity equation and momentum equation of the two-dimensional incompressible Navier-Stokes equations integrated along the water depth are as follows:

[0015] Continuity equation:

[0016]

[0017] Momentum equation:

[0018]

[0019] In the formula, h for water depth; t is the time step; , Horizontal x , Vertical y Flow rate in direction; u , v are the vertical average flow velocity in x , y Directional weight; z is the water level; g is the acceleration due to gravity; C is Xiecai coefficient; v t is the turbulent viscosity coefficient.

[0020] Furthermore, in step 4, the fitting Gaussian function formula is as follows:

[0021]

[0022] In the formula, x It is an environmental factor within a natural year; y It is the growth state of the plant; u is the point with the most suitable growth threshold; when the environmental factor value is u Plants grow best when u The value of is equal to the peak value of the Gaussian curve; t is the range of plant adaptation, which refers to the ability of the community to adapt to environmental factors. t Or the shape parameter of the Gaussian curve, t The larger the value of is, the more dispersed the data distribution is and the flatter the curve is. y 0 is a constant.

[0023] Furthermore, in step 5, according to the Gaussian function fitting result, the parameter u and t To calculate the ecological threshold, the environmental gradient is divided into the most suitable ( u - t , u + t ) and restrictions (< u -2 t ,> u +2 t ) two intervals.

[0024] Furthermore, in step six, the area for simulation and prediction of the required plant type is divided into cellular automaton grids, and the study area is divided into square grids. According to the principle of cellular automaton, the transfer function of the cell is constructed as shown in the following formula:

[0025]

[0026] In the formula, , The central cell at time t and time t +1 status result, N is the neighborhood of the cell, f A state transition rule function that represents the interaction between cells in the neighborhood;

[0027] The transfer matrix is ​​calculated based on the plant type of the current year and the annual flooding time. The calculation formula is as follows:

[0028]

[0029] Where, 0≤ P ij <1; ;P ij ( i , j =1, 2,..., n ) is the plant type in the current year i Convert to j The probability of P is the comprehensive state transition probability matrix; n is the number of plant types; S t and S t+1 They are t and t +1 year of plant type.

[0030] Furthermore, in step 7, the suitability map was prepared using the fuzzy set membership function according to the annual flooding time of the calculation year and the ecological thresholds of different plant types, and the J-type symmetric function in the fuzzy set membership function was used to generalize the suitability of different plant types; for the simulation results, the Kappa coefficient was used to verify the accuracy, and the calculation formula of the Kappa coefficient is as follows:

[0031]

[0032] In the formula, x 0 is the ratio of the predicted correct elements to the actual total elements, x c To predict the proportion of correct elements in a random state; the Kappa coefficient calculation results are divided into five groups to represent different levels of consistency: 0.0~0.2 extremely low consistency, 0.2~0.4 general consistency, 0.4~0.6 medium consistency, 0.6~0.8 high consistency and 0.8~1.0 almost complete consistency.

[0033] In the second aspect, the present invention provides a lake wetland plant type prediction system that integrates a two-dimensional hydrodynamic model and CA-Markov, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements the above-mentioned lake wetland plant type prediction method that integrates the two-dimensional hydrodynamic model and CA-Markov.

[0034] The present invention has the following beneficial effects: a method and system for predicting plant types in lake wetlands by integrating a two-dimensional hydrodynamic model and CA-Markov of the present invention obtains basic data related to the hydrology and meteorology of lake wetlands, wherein the basic data include lake topography data, daily-scale hydrological data, daily-scale meteorological data and plant type data of lake wetlands; based on the basic data, the upper and lower boundaries of the study area are set, the roughness parameters of the model are defined, a two-dimensional hydrodynamic model is constructed by using the two-dimensional incompressible Navier-Stokes equation, the annual flooding time is used as the hydrological situation indicator, and the two-dimensional The water depth data of each hydrodynamic grid of the lake wetland obtained by the hydrodynamic model is used to calculate the annual inundation time; the Gaussian function is used to fit the relationship between the hydrological situation indicators and different plant types; based on the fitting results of the Gaussian function, the hydrological situation ecological thresholds of different plant types are calculated; based on the plant types and annual inundation time of the current year, the cellular automaton-Markov method is used to establish the transfer matrix of the plant types in the next period; the fuzzy set membership function is used to make the hydrological situation ecological threshold suitability map, and it is used as the constraint condition of the transfer matrix, and finally the plant type at the next moment is obtained, which can provide important support for the accurate prediction of wetland plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A flow chart of a method for predicting lake wetland plant types that integrates a two-dimensional hydrodynamic model and CA-Markov provided by the present invention;

[0037] Figure 2 It is a schematic diagram of ecological threshold based on Gaussian fitting function of plant type;

[0038] Figure 3 It is the membership function diagram of J-type symmetric fuzzy sets. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0040] See also Figure 1 The present invention provides a lake wetland plant type prediction method integrating a two-dimensional hydrodynamic model and CA-Markov, comprising:

[0041] Step 1: Obtain basic data related to lake wetland hydrology and meteorology, wherein the basic data includes lake topography data, daily-scale hydrological data, daily-scale meteorological data, and plant type data of the lake wetland.

[0042] Specifically, the daily-scale hydrological data includes daily-scale flow data and water level data; the daily-scale meteorological data includes daily-scale rainfall data, temperature data, wind speed data, etc.; in this example, the lake wetland plant types are divided into southern reed, carex and polygonum, and this classification type reflects the foraging plant types of wetland birds.

[0043] Step 2: Based on the basic data, the upper and lower boundaries of the study area are set, the roughness parameters of the model are defined, and a two-dimensional hydrodynamic model is constructed using the two-dimensional incompressible Navier-Stokes equations.

[0044] Specifically, the continuity equation and momentum equation of the two-dimensional incompressible Navier-Stokes equation integrated along the water depth are as follows:

[0045] Continuity equation:

[0046]

[0047] Momentum equation:

[0048]

[0049] In the formula, h for water depth; t is the time step; , Horizontal x , Vertical y Flow rate in direction; u , v are the vertical average flow velocity in x , y Directional weight; z is the water level; g is the acceleration due to gravity; C is Xiecai coefficient; v t is the turbulent viscosity coefficient.

[0050] Step three, using the annual flooding time as a hydrological situation indicator, and calculating the annual flooding time based on the water depth data of each hydrodynamic grid of the lake wetland obtained by the two-dimensional hydrodynamic model.

[0051] A large number of studies have shown that plants are most sensitive to changes in flooding. A certain period of flooding can meet the basic requirements for plant growth and development. If the flooding time is too long, plant growth will be inhibited. There is an obvious ecological threshold between plant growth and hydrological conditions. Considering that the flooding time has the most significant impact on plant growth, this example uses the annual flooding time as a representative hydrological indicator.

[0052] Step 4: Use Gaussian function to fit the relationship between hydrological indicators and different plant types.

[0053] Specifically, the spatial response relationship between hydrological regime indicators and different plant community types was fitted based on the Gaussian model, such as Figure 2 Among them, the independent variable is the hydrological situation, and the dependent variable is the area statistics of each plant type. The fitted Gaussian function formula is as follows:

[0054]

[0055] In the formula, x is an environmental factor within a natural year, in this case, the annual flooding time; y is the growth status of the plants, in this case the plant type area of ​​the entire lake; u is the point with the most suitable growth threshold; when the environmental factor value is u Plants grow best when u The value of is equal to the peak value of the Gaussian curve; t is the range of plant adaptation, which refers to the ability of the community to adapt to environmental factors. t Or the shape parameter of the Gaussian curve, t The larger the value of is, the more dispersed the data distribution is and the flatter the curve is, and vice versa; A and y 0 are all constants obtained by solving the equation.

[0056] Step 5: Calculate the ecological threshold of hydrological conditions for different plant types based on the fitting results of the Gaussian function.

[0057] Specifically, according to the Gaussian function fitting results, the parameters u and t To calculate the ecological threshold, the environmental gradient is divided into the most suitable ( u - t , u + t ) and restrictions (< u -2 t ,> u +2 t ) two intervals. The fitting results and ecological thresholds of three different plant types and annual flooding time in this example are shown in the following table:

[0058] Table 1 Fitting results and ecological thresholds of the relationship between different plant types and flooding time

[0059]

[0060] Step six, based on the plant types of the current year and the annual flooding time, the cellular automaton-Markov method is used to establish the transition matrix of the plant types in the next period.

[0061] Specifically, the area where the required plant type is simulated and predicted is divided into cellular automaton grids, and the study area is divided into square grids. According to the principle of cellular automaton, the transfer function of the cell is constructed as shown in the following formula:

[0062]

[0063] In the formula, , The central cell at time t and time t +1 status result, N is the neighborhood of the cell, f Represents the state transition rule function of the interaction between cell units in the neighborhood; in this example, a 5×5 filter is used to define the cellular neighborhood, that is, the extended Moore neighborhood model of two-dimensional CA. The state transition of the central cell is significantly affected by the rectangular space composed of the 5×5 cells around it.

[0064] The transfer matrix is ​​calculated based on the plant type of the current year and the annual flooding time. The calculation formula is as follows:

[0065]

[0066] Where, 0≤ P ij <1; ; P ij ( i , j =1, 2,..., n ) is the plant type in the current year i Convert to j The probability of P is the comprehensive state transition probability matrix; n is the number of plant types; S t and S t+1 They are t and t +1 year of plant type.

[0067] In this example, three types of plants grow in the lake wetland area: southern reed, knotweed and carex. All cells in the space are divided into three categories according to the plant type of the current year; then, the probability of a certain plant type growing three plants on each cell in the area is counted. This probability is calculated using the Gaussian curve formula based on the specific value of the annual inundation time of each cell position. Each cell is calculated three times, and the proportion of its value is the probability of the plant occurring in the cell; finally, the probabilities of each cell in the area of ​​the plant type are added together to obtain the overall probability of the plant type in the current year being converted into the three plant communities in the next year, and the probability transfer matrix is ​​obtained.

[0068] Step seven, use the fuzzy set membership function to make the hydrological situation ecological threshold suitability map, and use it as the constraint condition of the transfer matrix to finally obtain the plant type at the next moment.

[0069] Specifically, the suitability map was made using fuzzy set membership functions according to the annual flooding time in the calculation year and the ecological thresholds of different plant types, and the J-type symmetric function in the fuzzy set membership function was used to generalize the suitability of different plant types, e.g. Figure 3 ; Among them, a~c are the variable control points where the probability of occurrence of this plant increases significantly, c~d are the variable control points with the highest probability of plant occurrence, and d~b are the variable control points where the probability of occurrence of this plant decreases significantly. The c and d values ​​are set as the optimal ecological threshold interval values, and the a and b values ​​are set as the restricted ecological interval values, as shown in Table 1. Finally, the occurrence probability is uniformly converted into standardized data within the range of 0~255. According to the above steps, the suitability maps of three plant types, southern reed, Polygonum aviculare and Carex, are calculated. For the simulation results, the Kappa coefficient is used to verify the accuracy. The calculation formula of the Kappa coefficient is as follows:

[0070]

[0071] In the formula, x 0 is the ratio of the predicted correct elements to the actual total elements, x c To predict the proportion of correct elements in a random state; the Kappa coefficient calculation results are divided into five groups to represent different levels of consistency: 0.0~0.2 extremely low consistency, 0.2~0.4 general consistency, 0.4~0.6 medium consistency, 0.6~0.8 high consistency and 0.8~1.0 almost complete consistency.

[0072] An embodiment of the present invention also provides a lake wetland plant type prediction system that integrates a two-dimensional hydrodynamic model and CA-Markov, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements the above-mentioned lake wetland plant type prediction method that integrates a two-dimensional hydrodynamic model and CA-Markov.

[0073] The embodiment of the present invention further provides a storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, some or all of the steps in each embodiment of the method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov provided by the present invention are implemented. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0074] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A lake wetland plant type prediction method integrating a two-dimensional hydrodynamic model and CA-Markov, characterized in that: include: Step 1: Obtain basic data related to lake wetland hydrology and meteorology, wherein the basic data includes lake topography data, daily-scale hydrological data, daily-scale meteorological data, and plant type data of the lake wetland; Step 2: Based on the basic data, the upper and lower boundaries of the study area are set, the roughness parameters of the model are defined, and a two-dimensional hydrodynamic model is constructed using the two-dimensional incompressible Navier-Stokes equation. The continuity equation and momentum equation formulas of the two-dimensional incompressible Navier-Stokes equation integrated along the water depth are as follows: Continuity equation: Momentum equation: In the formula, h for water depth; t is the time step; , Horizontal x , Vertical y Flow rate in direction; u , v are the vertical average flow velocity in x , y Directional weight; z is the water level; g is the acceleration due to gravity; C is Xiecai coefficient; v t is the turbulent viscosity coefficient; Step 3, using the annual flooding time as a hydrological indicator, and calculating the annual flooding time according to the water depth data of each hydrodynamic grid of the lake wetland obtained by the two-dimensional hydrodynamic model; Step 4: Use Gaussian function to fit the relationship between hydrological indicators and different plant types; Step 5: Calculate the ecological threshold of hydrological conditions of different plant types based on the fitting results of the Gaussian function; Step 6: Based on the plant types of the current year and the annual flooding time, the cellular automaton-Markov method is used to establish the transition matrix of the plant types in the next period; Step seven, use the fuzzy set membership function to make the hydrological situation ecological threshold suitability map, and use it as the constraint condition of the transfer matrix to finally obtain the plant type at the next moment.

2. The method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as claimed in claim 1, characterized in that: In step one, the daily-scale hydrological data includes daily-scale flow data and water level data; the daily-scale meteorological data includes daily-scale rainfall data, temperature data, and wind speed data.

3. The method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as claimed in claim 1, characterized in that: In step 4, the fitted Gaussian function formula is as follows: In the formula, x It is an environmental factor within a natural year; y It is the growth state of the plant; u is the point with the most suitable growth threshold; when the environmental factor value is u Plants grow best when u The value of is equal to the peak value of the Gaussian curve; t is the range of plant adaptation, which refers to the ability of the community to adapt to environmental factors. t Or the shape parameter of the Gaussian curve, t The larger the value of is, the more dispersed the data distribution is and the flatter the curve is. y 0 is a constant.

4. The method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as claimed in claim 1, characterized in that: In step 5, according to the Gaussian function fitting results, the parameters u and t To calculate the ecological threshold, the environmental gradient is divided into the most suitable ( u - t , u + t ) and restrictions (< u -2 t ,> u +2 t ) two intervals.

5. The method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as claimed in claim 1, characterized in that: In step 6, the area where the required plant type is simulated and predicted is divided into cellular automata grids, and the study area is divided into square grids. According to the principle of cellular automata, the transfer function of the cell is constructed as shown in the following formula: In the formula, , The central cell at time t and time t +1 status result, N is the neighborhood of the cell, f A state transition rule function that represents the interaction between cells in the neighborhood; The transfer matrix is ​​calculated based on the plant type of the current year and the annual flooding time. The calculation formula is as follows: Where, 0≤ P ij <1; ; P ij ( i , j =1, 2,..., n ) is the plant type in the current year i Convert to j probability; P is the comprehensive state transition probability matrix; n is the number of plant types; S t and S t+1 They are t and t +1 year of plant type.

6. The method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as claimed in claim 1, characterized in that: In step 7, the suitability map is made using the fuzzy set membership function according to the annual flooding time of the calculation year and the ecological thresholds of different plant types. The J-type symmetric function in the fuzzy set membership function is used to generalize the suitability of different plant types. For the simulation results, the Kappa coefficient is used to verify the accuracy. The calculation formula of the Kappa coefficient is as follows: In the formula, x 0 is the ratio of the predicted correct elements to the actual total elements, xc To predict the proportion of correct elements in a random state; the Kappa coefficient calculation results are divided into five groups to represent different levels of consistency: 0.0~0.2 extremely low consistency, 0.2~0.4 general consistency, 0.4~0.6 medium consistency, 0.6~0.8 high consistency and 0.8~1.0 almost complete consistency.

7. A lake wetland plant type prediction system integrating a two-dimensional hydrodynamic model and CA-Markov, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for predicting lake wetland plant types by integrating a two-dimensional hydrodynamic model and CA-Markov as described in any one of claims 1 to 6 is implemented.

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