CN117171128A - Aquatic organism protection threshold identification method based on four-water coupling model - Google Patents
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Abstract
The application discloses an aquatic organism protection threshold identification method based on a four-water coupling model, which comprises the following steps: s1, constructing a watershed hydrological model, and then inputting rainfall data to obtain runoff output data; s2, constructing a river hydrodynamic water quality model, and then inputting water quantity data, water level data and water quality concentration data to obtain water quantity output data, flow velocity output data, water level output data, flow direction output data and water quality concentration output data; s3, determining aquatic organism sampling points, and acquiring hydrologic, hydrodynamic force and water quality data corresponding to the aquatic organism sampling points through a river basin hydrologic model and a river channel hydrodynamic force water quality model; and S4, extracting hydrologic, hydrodynamic force and water quality data matched with the aquatic organism sampling points from the model, constructing a random forest-based multi-element coupling model, and identifying the aquatic organism protection threshold. Through the design, the method and the device have the technical effect of solving the data matching in the aquatic organism protection threshold identification process.
Description
Technical Field
The application belongs to the field of water ecology, and particularly relates to an aquatic organism protection threshold identification method based on a four-water coupling model.
Background
Four-water coupling generally refers to hydrologic, hydrodynamic, water quality and aquatic organism coupling, and basically covers main processes of water ecological simulation, and some coupling models at present mainly consider the first three processes, namely hydrologic, hydrodynamic and water quality processes, or consider the latter three processes, namely hydrodynamic, water quality and aquatic organism processes. Either way, this results in a loss of the water ecological simulation process. The lack of the water ecological simulation process makes data acquisition difficult, particularly hydrological data, which is generally difficult to match with the aquatic biological data, and the reasons for the defect are various, and the main reasons are lack of basic data, which is insufficient for constructing a model; or the model technology method is difficult to break through and is not applicable. In order to better match the data of aquatic creatures, the response data needs to be acquired by means of a multi-process model so as to carry out subsequent analysis.
The method and the device for dynamically simulating and dispatching the coupled water quantity-water quality-aquatic organism aggregate community disclosed in Chinese patent CN115034425A comprise the following steps: s1, determining a research scene, collecting measured data of a research area, and determining an influence factor and a decision variable of population dynamics; s2, constructing a coupling water quantity-water quality-aquatic organism aggregate community dynamic model by considering the interaction relation among multiple populations, the water quantity and water quality effect, population migration in and migration out and the influence of other factors; s3, calculating the ecological niche width, the environment optimal value and the competition coefficient parameter value in the integrated community dynamic model based on a niche number regression method and an ecological niche theory; and S4, simulating the cooperative evolution process of a plurality of group systems in the aggregated community, and determining the influence degree of water quantity and water quality on the dynamics of the aggregated community. However, the designed model process lacks a physical mechanism and mainly depends on the establishment of a response relation between measured data.
The method for preparing the water quality safety threshold value of the surface water of the aquatic organism for protecting antimony disclosed in Chinese patent CN109342675A comprises the following steps: s1, collecting aquatic organism toxicity data of antimony; s2, deducing a water quality reference value of the protected aquatic organisms of the stibium; and S3, preparing a water quality safety threshold value of the surface water of the protected aquatic organism by using the antimony, and simultaneously, providing a method for evaluating the water quality safety by using the water quality safety threshold value of the surface water of the protected aquatic organism by using the antimony. The method mainly adopts a derivation model as a logic-substance distribution model for obtaining the water quality standard value of the antimony protected aquatic organisms, but mainly considers the influence of one index on the organisms, ignores the comprehensive influence of other indexes on the index or the aquatic organisms, namely, excludes other factors in the factor screening process.
Because the existing aquatic ecology simulation lacks a multi-process simulation technology and has the problem that parameters are difficult to acquire in the process of identifying the aquatic organism protection threshold value, a new aquatic organism protection threshold value identification method is urgently needed to be designed so as to solve the problem.
Disclosure of Invention
Aiming at the problems that the existing aquatic ecology simulation lacks a multi-process simulation technology and parameters are difficult to acquire in the process of identifying the aquatic organism protection threshold, the application designs the aquatic organism protection threshold identification method based on the four-water coupling model, the data of the corresponding aquatic organism sampling points are acquired by means of the hydrological model and the hydrodynamic water quality model, and then the protection threshold is calculated, so that the technical effect of data matching in the aquatic organism protection threshold identification process is achieved.
A aquatic organism protection threshold identification method based on a four-water coupling model comprises the following steps:
s1, constructing a drainage basin hydrological model based on a TVGM model, and then inputting rainfall data to obtain runoff output data;
s2, constructing a river channel hydrodynamic water quality model based on runoff data and measured water quality data output by a river basin hydrological model, and then inputting water quantity data, water level data and water quality concentration data to acquire water quantity output data, flow velocity output data, water level output data, flow direction output data and water quality concentration output data;
s3, determining aquatic organism sampling points, and acquiring hydrologic, hydrodynamic force and water quality data corresponding to the aquatic organism sampling points through a river basin hydrologic model and a river channel hydrodynamic force water quality model;
and S4, constructing a multi-element coupling model based on random forests according to hydrology, hydrodynamic force and water quality data, and identifying the aquatic organism protection threshold.
Preferably, the method for constructing the drainage basin hydrological model in the step S1 includes the following steps:
step S101, firstly, collecting basin elevation DEM data, rainfall stations, rainfall data, basin hydrologic stations and flow data;
step S102, dividing a river basin range in Arcgis software by using DEM data, and counting river basin information, wherein the river basin information comprises a river basin name, a river basin area, longitude and latitude, and a memory length; finally, the information is input into a txt document;
step S103, dividing the acquired rainfall data and flow data into three parts, wherein one part of the data is used for calibration, one part of the data is used for verification, and the last part of the data is used for simulation; storing the processed data as txt documents;
and step S104, the data processed in the step S103 are brought into a TVGM model, and the basin hydrological model construction is completed.
Preferably, the construction method of the river hydrodynamic water quality model in the step S2 includes the following steps:
step S201, acquiring data of river channel boundaries, river channel topography, flow, water level, water quality concentration and water temperature;
step S202, importing the acquired high-resolution satellite map into arcgis software, and sequentially outlining by using editing tools of the arcgis software according to the river range in the satellite map to obtain a surface map layer of the river, namely a river boundary; importing the drawn river boundary into globalmapacer software, and converting the river boundary into a format recognizable by Delft3D software; dividing river grids in Delft3D, and exporting the river grids into grid formats; the grid file required by the construction of the river hydrodynamic water quality model can be obtained;
step S203, importing the generated grid file into an EFDC model to obtain coordinates of four corners of the grid; respectively taking an average value of coordinates X of four corners and an average value of four coordinates Y, and calculating coordinates of a grid center point; according to the sequence of the upstream and downstream of the grids, calculating the elevation of the central point of each grid based on a small amount of measured section data and the average slope of the topography; inputting the calculated grid points into an EFDC model to obtain river terrain required by the model;
and S204, inputting the acquired water quality concentration, water level data, the processed river terrain and the flow obtained by TVGM model simulation into the EFDC model, and thus completing the construction of the river hydrodynamic water quality model.
Preferably, the step S3 includes the steps of:
step S301, according to the point location information of the actually measured aquatic organism sampling points, the aquatic organism sampling points are dropped to the corresponding river grid midpoints;
step S302, extracting data from the model; after the river hydrodynamic water quality model is built, water quantity and water quality data of each grid are obtained based on model simulation, and are directly derived from the grids of the EFDC model.
Preferably, the step S4 includes the steps of:
s401, integrating the data extracted in the steps S1-S3 according to the point positions and the index types;
and step S402, establishing a random forest-based multi-element coupling model by using the data, and calculating an index threshold value based on the IBI index.
The application has the following advantages and effects:
according to the aquatic organism protection threshold identification method based on the four-water coupling model, through the mode that the hydrologic model and the hydrodynamic water quality model are used for acquiring data corresponding to the aquatic organism sampling points and then calculating the protection threshold, matching of the aquatic ecology monitoring data and the hydrologic hydrodynamic water quality factor data is achieved, finally multi-process multi-factor coupling modeling is achieved, the protection threshold of the aquatic organism is identified, and complex influences of various indexes on the aquatic organism are comprehensively reflected.
The foregoing description is only an overview of the present application, and is intended to provide a better understanding of the technical means of the present application, so that the present application may be practiced according to the teachings of the present specification, and so that the above-mentioned and other objects, features and advantages of the present application may be better understood, and the following detailed description of the preferred embodiments of the present application will be presented in conjunction with the accompanying drawings.
The above and other objects, advantages and features of the present application will become more apparent to those skilled in the art from the following detailed description of the specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of an aquatic organism protection threshold identification method based on a four-water coupling model;
FIG. 2 is a state diagram of the information entered as txt document in step S102 provided by the present application;
FIG. 3 is a TVGM basin hydrological model diagram provided by the application;
FIG. 4 is a conceptual diagram of a simplified basin hydrologic model provided by the present application;
FIG. 5 is a graph showing the effect of inputting processed terrain, TVGM simulated flow, acquired water quality concentration and water level data into an EFDC model;
FIG. 6 is an effect diagram of the construction of the EFDC river hydrodynamic water quality model provided by the application;
FIG. 7 is a diagram of directly derived data locations in a grid of an EFDC model according to the present application
FIG. 8 is a graph of index threshold recognition results based on IBI index provided by the application;
FIG. 9 is a simulation result diagram of a watershed hydrological model provided by the application;
FIG. 10 is a graph of the flow and partial water quality index calibration verification conditions provided by the application;
fig. 11 is a key indicator threshold identification flow chart for large benthos biological integrity protection provided by the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. In the following description, specific details such as specific configurations and components are provided merely to facilitate a thorough understanding of embodiments of the application. It will therefore be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, descriptions of well-known functions and constructions are omitted in the embodiments for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "this embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the "one embodiment" or "this embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: the terms "/and" herein describe another associative object relationship, indicating that there may be two relationships, e.g., a/and B, may indicate that: the character "/" herein generally indicates that the associated object is an "or" relationship.
The term "at least one" is herein merely an association relation describing an associated object, meaning that there may be three kinds of relations, e.g., at least one of a and B may represent: a exists alone, A and B exist together, and B exists alone.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion.
Example 1
The embodiment mainly introduces a design of an aquatic organism protection threshold identification method based on a four-water coupling model, which comprises the following steps:
s1, constructing a drainage basin hydrological model based on a TVGM model, and then inputting rainfall data to obtain runoff output data; the method for constructing the drainage basin hydrologic model in the step S1 comprises the following steps of:
step S101, firstly collecting basin elevation DEM data, rainfall stations, rainfall data (daily rainfall), basin hydrologic stations and flow data (daily scale flow); DEM data can be acquired from websites of open sources of the Internet, and rainfall and flow data can be acquired from hydrological annual notes;
step S102, dividing a drainage basin range (comprising a full drainage basin and subunits) in Arcgis software by using DEM data, and counting drainage basin information, wherein the drainage basin information comprises a drainage basin name, a drainage basin area, longitude and latitude, and a memory length (the ratio of the area of the drainage basin to the perimeter); finally, the information is input into a txt document as shown in fig. 2:
step S103, dividing the acquired rainfall data and flow data into three parts, wherein one part of the data is used for calibration, one part of the data is used for verification, and the last part of the data is used for simulation; storing the processed data as txt documents; if rainfall and flow data in 2013-2022 ten years exist, data in 2013-2020 can be used as calibration model parameters, then data in 2021-2022 are used for verifying the model parameters, and third part of data, namely rainfall data in future years, can be used for simulating hydrological runoff data in future years after the parameter calibration verification is completed;
step S104, the data processed in the step S103 are brought into a TVGM model (a basin nonlinear rainfall runoff model), and the basin hydrologic model construction is completed as shown in FIG. 3.
Referring to fig. 4, fig. 4 is a conceptual diagram of a simple basin hydrologic model, wherein the left diagram shows a divided basin range and distribution of water systems and hydrologic stations in the basin, and the flow data of each section, namely, the right time and runoff diagram, can be obtained by inputting rainfall data based on the basin hydrologic model. A hydrologic model of the whole river basin is established firstly, Q is simulated (Q represents the total flow of the outlet section of the river basin), then the flow of the subunits (Q0-Q4) is simulated, and thus the flow process (Qi) of the main flow and each tributary can be obtained.
S2, constructing a river channel hydrodynamic water quality model based on runoff data and measured water quality data output by a river basin hydrological model, and then inputting water quantity data, water level data and water quality concentration data to acquire water quantity output data, flow velocity output data, water level output data, flow direction output data and water quality concentration output data; the construction method of the river hydrodynamic water quality model comprises the following steps:
step S201, the data to be collected includes river channel boundary (shp, which can be drawn from an ovine map), river channel topography (xyz, which can be obtained by performing secondary processing based on DEM data extraction), flow (obtained based on hydrological annual survey or TVGM), water level (hydrological annual survey), water quality concentration (COD, ammonia nitrogen, TP, nitrate, etc.), which can be obtained from a national surface water quality automatic monitoring real-time data distribution system), water temperature (hydrological annual survey);
step S202, importing arcgis software according to the acquired high-resolution satellite map, and sequentially outlining by utilizing an editing tool carried by arcgis to obtain a surface layer of the river, namely a river boundary, according to the river range in the satellite map; importing the depicted river boundary into a globalmapacer, and converting the globalmapacer into a format which can be identified by Delft 3D; dividing river grids in Delft3D, and exporting the river grids into grid formats; the grid file required by the model construction can be obtained;
step S203, importing EFDC through the generated grid to obtain coordinates of four corners of the grid; respectively taking the average value of four X and the average value of four Y through the coordinates of four corners of the grid, and calculating the coordinates of the central point of the grid; according to the sequence of the upstream and downstream of the grids, calculating the elevation of the central point of each grid based on a small amount of measured section data and the average slope of the topography; inputting the calculated grid points into an EFDC model to obtain river terrain required by the model;
step S204, inputting the acquired water quality concentration, water level data, processed river terrain and flow obtained by TVGM model simulation into an EFDC model, as shown in FIG. 5, wherein Qi represents upstream and tributary flows of a main flow, namely, the flow simulated by the TVGM model, C represents concentration values of different indexes of the main flow and the tributary, and WL represents the water level of the downstream; the construction of the river hydrodynamic water quality model can be completed.
Step S205, as shown in FIG. 6, the construction of the EFDC river hydrodynamic water quality model can be completed through the above process, and the calibration verification work of the model is completed through the comparison of actual measurement and simulation data. Based on the model after calibration and verification, the data such as flow, water level, water quality, flow velocity and the like of any grid and any section can be obtained.
S3, determining aquatic organism sampling points, and acquiring hydrologic, hydrodynamic force and water quality data corresponding to the aquatic organism sampling points through a river basin hydrologic model and a river channel hydrodynamic force water quality model; the data of the corresponding point is extracted mainly through the hydrologic-hydrodynamic water quality model established above, and the point is the aquatic organism sampling point, because the flow, the flow velocity, the water depth and part of water quality data of the corresponding point are not necessarily acquired when the aquatic organism is generally sampled, but the data of the corresponding time of the aquatic organism sampling point can be directly simulated through the establishment of the hydrodynamic water quality model. The steps are as follows:
step S301, determining aquatic organism sampling points. The process mainly includes that the aquatic organism sampling points are dropped into corresponding grids according to the actually measured point position information, so that various data of which time period and which grid in the extraction model can be clearly known;
step S302, extracting data from the slave model. After the EFDC model is built, water quantity and water quality data of each grid can be obtained based on model simulation, and can be directly derived from the grids of the EFDC model. As shown in fig. 7, the hydrologic and water quality data of the aquatic organism sampling points S1, S2, S3, S4 can be calculated by a model constructed from hydrologic and water quality point data of H1, H2, W1, W2, etc. Including flow, water level, water depth, flow rate, water temperature, COD, ammonia nitrogen, DO, total phosphorus, nitrate, etc.
S4, extracting hydrologic, hydrodynamic force and water quality elements matched with the aquatic organism sampling points from the model, constructing a random forest-based multi-element coupling model, and identifying an aquatic organism protection threshold; the method mainly uses a random analysis package of R language, a random forest model among multiple elements can be directly built by using the package, contribution of each element to the diversity of aquatic organisms is identified, and then a protection threshold is determined. The step S4 includes the steps of:
s401, integrating the data extracted in the steps S1-S3 according to the point positions and the index types; the consolidated data format is as follows. S1-Sn are the numbers of sampling points of aquatic organisms, F1-Fn are various indexes extracted by a model and other indexes extracted by a non-model, IBI indexes are aquatic organism diversity indexes, and a random forest model is used for establishing the relation between F1-Fn and IBI indexes;
step S402, establishing a random forest-based multi-element coupling model by using the data, and calculating an index threshold value based on an IBI index; as shown in fig. 8, ABCD is the level of the integrity of the aquatic organism divided based on the IBI index, respectively, the higher the level, the stronger the integrity of the aquatic organism, and if D is the highest level, the stronger the integrity of the aquatic organism when the index F1 is smaller than the a value and the index F3 is smaller than the c value, and thus the index thresholds protecting the integrity are a and c.
According to the aquatic organism protection threshold identification method based on the four-water coupling model, through the mode that the hydrologic model and the hydrodynamic water quality model are used for acquiring data corresponding to the aquatic organism sampling points and then calculating the protection threshold, matching of the aquatic ecology monitoring data and the hydrologic hydrodynamic water quality factor data is achieved, finally multi-process multi-factor coupling modeling is achieved, the protection threshold of the aquatic organism is identified, and complex influences of various indexes on the aquatic organism are comprehensively reflected.
Example 2
Based on the above embodiment 1, this embodiment mainly describes a four-water-coupling-model-based aquatic organism protection threshold identification method designed by the present application, and a protection threshold of benthos in a river basin is analyzed by searching in the river basin.
1. And (3) constructing a watershed hydrologic model: the verification result of the drainage basin hydrologic model calibration constructed in the step S1 is shown in fig. 9.
2. Constructing a river hydrodynamic water quality model: and (2) calibrating and verifying the flow data output in the step (S1) and the collected river water level and water quality data by utilizing the hydrodynamic water quality model constructed in the step (S2) as shown in FIG. 10.
3. And (3) data extraction: according to step S3, the summary of the model-based aquatic organism sampling point correspondence data and other source data is shown in the following table, wherein NO3, NH4, COD, WT (water temperature), SD (transparency) and Q (flow) are obtained by the EFDC model, and AT (air temperature) and RF (rainfall) are obtained from the weather website. IBI is the integrity index of large benthos, and is obtained through investigation and monitoring.
4. Random forest model construction and threshold identification based on aquatic organism integrity: the index threshold for large benthonic organism integrity protection identified from the random forest established in step 4, as shown in fig. 11, is more advantageous for aquatic organism protection when the aquatic organism integrity index is higher. Therefore, based on the model, it can be found that when the air temperature is less than 14.78 ℃ and the ammonia nitrogen concentration is less than 0.586mg/l, the large benthonic integrity index is highest, that is, if the protection of the large benthonic integrity is to be enhanced in the river basin, besides the influence of the natural air temperature, the ammonia nitrogen concentration needs to be reduced to be less than 0.586mg/l, so that at= 14.788 ℃ and nh4=0.586 mg/l are the protection threshold values of the large benthonic integrity of the river basin.
The above description is only of the preferred embodiments of the present application and it is not intended to limit the scope of the present application, but various modifications and variations can be made by those skilled in the art. Variations, modifications, substitutions, integration and parameter changes may be made to these embodiments by conventional means or may be made to achieve the same functionality within the spirit and principles of the present application without departing from such principles and spirit of the application.
Claims (5)
1. The aquatic organism protection threshold identification method based on the four-water coupling model is characterized by comprising the following steps of:
s1, constructing a drainage basin hydrological model based on a TVGM model, and then inputting rainfall data to obtain runoff output data;
s2, constructing a river channel hydrodynamic water quality model based on runoff data and measured water quality data output by a river basin hydrological model, and then inputting water quantity data, water level data and water quality concentration data to acquire water quantity output data, flow velocity output data, water level output data, flow direction output data and water quality concentration output data;
s3, determining aquatic organism sampling points, and acquiring hydrologic, hydrodynamic force and water quality data corresponding to the aquatic organism sampling points through a river basin hydrologic model and a river channel hydrodynamic force water quality model;
and S4, constructing a multi-element coupling model based on random forests according to hydrology, hydrodynamic force and water quality data, and identifying the aquatic organism protection threshold.
2. The aquatic organism protection threshold value recognition method based on the four-water coupling model according to claim 1, wherein the basin hydrologic model construction method in step S1 comprises the following steps:
step S101, firstly, collecting basin elevation DEM data, rainfall stations, rainfall data, basin hydrologic stations and flow data;
step S102, dividing a river basin range in Arcgis software by using DEM data, and counting river basin information, wherein the river basin information comprises a river basin name, a river basin area, longitude and latitude, and a memory length; finally, the information is input into a txt document;
step S103, dividing the acquired rainfall data and flow data into three parts, wherein one part of the data is used for calibration, one part of the data is used for verification, and the last part of the data is used for simulation; storing the processed data as txt documents;
and step S104, the data processed in the step S103 are brought into a TVGM model, and the basin hydrological model construction is completed.
3. The aquatic organism protection threshold value recognition method based on the four-water coupling model according to claim 1, wherein the method for constructing the river hydrodynamic water quality model in the step S2 comprises the following steps:
step S201, acquiring data of river channel boundaries, river channel topography, flow, water level, water quality concentration and water temperature;
step S202, importing the acquired high-resolution satellite map into arcgis software, and sequentially outlining by using editing tools of the arcgis software according to the river range in the satellite map to obtain a surface map layer of the river, namely a river boundary; importing the drawn river boundary into globalmapacer software, and converting the river boundary into a format recognizable by Delft3D software; dividing river grids in Delft3D, and exporting the river grids into grid formats; the grid file required by the construction of the river hydrodynamic water quality model can be obtained;
step S203, importing the generated grid file into an EFDC model to obtain coordinates of four corners of the grid; respectively taking an average value of coordinates X of four corners and an average value of four coordinates Y, and calculating coordinates of a grid center point; according to the sequence of the upstream and downstream of the grids, calculating the elevation of the central point of each grid based on a small amount of measured section data and the average slope of the topography; inputting the calculated grid points into an EFDC model to obtain river terrain required by the model;
and S204, inputting the acquired water quality concentration, water level data, the processed river terrain and the flow obtained by TVGM model simulation into the EFDC model, and thus completing the construction of the river hydrodynamic water quality model.
4. The aquatic organism protection threshold identification method based on the four-water coupling model according to claim 1, wherein the step S3 comprises the steps of:
step S301, according to the point location information of the actually measured aquatic organism sampling points, the aquatic organism sampling points are dropped to the corresponding river grid midpoints;
step S302, extracting data from the model; after the river hydrodynamic water quality model is built, water quantity and water quality data of each grid are obtained based on model simulation, and are directly derived from the grids of the EFDC model.
5. The aquatic organism protection threshold identification method based on the four-water coupling model according to claim 1, wherein the step S4 comprises the steps of:
s401, integrating the data extracted in the steps S1-S3 according to the point positions and the index types;
and step S402, establishing a random forest-based multi-element coupling model by using the data, and calculating an index threshold value based on the IBI index.
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