River network water environment main control unit identification method and system based on Monte Carlo sampling

Abstract

The invention provides a river network water environment main control unit identification method and system based on Monte Carlo sampling. The method comprises the following steps: step S01, dividing a river channel in a river network region into a plurality of control units; s02, establishing a drainage basin river network model; s03, generating a plurality of random disturbance coefficients for each control unit through Monte Carlo sampling; step S04, each control unit performs water quality simulation based on the plurality of random disturbance coefficients, and correspondingly generates a plurality of pollution load scenes; inputting a plurality of pollution load scenes into the drainage basin river network model to obtain a target section water quality concentration; and S05, selecting water quality indexes, establishing a response relationship between the plurality of random disturbance coefficients of each control unit and the water quality concentration of the target section, and determining the influence degree of each control unit on the water quality of the target section according to the response relationship, thereby identifying the main control unit. The method can provide guidance for accurate identification of the main control unit in the drainage basin environmental governance process.

Description

Technical Field

[0001] The present invention belongs to the technical field of water environment impact assessment and main control unit identification, and particularly relates to a method and system for identifying main control units of river network water environment based on Monte Carlo sampling. Background Art

[0002] In water environment management, identifying the main control units that significantly affect the water quality of key sections is the key to formulating effective pollution control strategies. However, existing technologies often struggle to accurately quantify the contributions of each control unit to water quality and lack systematic analysis methods, resulting in low pertinence and efficiency of pollution control measures. Summary of the Invention

[0003] Object of the Invention: The technical problem to be solved by the present invention is to provide a method and system for identifying main control units of river network water environment based on Monte Carlo sampling in view of the deficiencies of the prior art, so as to accurately identify the control units in the basin that significantly affect the water quality of key sections and provide a scientific basis for water environment management and pollution control.

[0004] To solve the above technical problem, in the first aspect, a method for identifying main control units of river network water environment based on Monte Carlo sampling is disclosed, including the following steps:

[0005] Step S01: Divide the river channels in the river network area into multiple control units;

[0006] Step S02: Establish a river network model of the basin;

[0007] Step S03: Generate multiple random perturbation coefficients for each control unit through Monte Carlo sampling;

[0008] Step S04: Each control unit performs water quality simulation based on multiple random perturbation coefficients to generate multiple pollution load scenarios correspondingly; input the multiple pollution load scenarios into the river network model of the basin to obtain the water quality concentration of the target section under the corresponding pollution load scenarios;

[0009] Step S05: Select water quality indicators to establish the response relationship between the multiple random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenarios, and determine the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units.

[0010] Further, the response relationship between the multiple random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario in step S05 is a linear response relationship. The establishment of the linear response relationship can, firstly, directly quantify the correlation between the perturbation coefficient and the water quality concentration of the target section in a mathematical way; secondly, characterize the correlation coefficient through the slope value, directly visualize the degree of correlation, and simplify the decision-making process; thirdly, simplify the mechanistic correlation of the complex water environment system and reduce the calculation cost.

[0011] The selection of the Monte Carlo sampling times mainly affects the accuracy of establishing the linear response relationship between the perturbation coefficient and the water quality concentration of the target section. The larger the Monte Carlo sampling times, the more accurate the response relationship, and the longer the calculation period is.

[0012] Further, in step S05, determining the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units includes:

[0013] Calculating the correlation coefficient between the random perturbation coefficient of each control unit and the water quality concentration of the target section according to the linear response relationship between the multiple random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario. A larger correlation coefficient corresponding to the control unit indicates a greater influence degree of the control unit on the water quality of the target section;

[0014] Sorting the correlation coefficients of all control units, and identifying the control units with a greater influence degree on the water quality of the target section as the main control units.

[0015] Further, the water quality indicators selected in step S05 include selecting the water quality indicators that exceed the standard in the basin. This method is mainly used to formulate regional water environment control strategies, and selecting the water quality indicators that exceed the standard in the basin among a large number of water quality indicators is more targeted and practical for control.

[0016] Further, in step S04, each control unit performs water quality simulation based on multiple random perturbation coefficients, and correspondingly generates multiple pollution load scenarios, including: multiplying the multiple random perturbation coefficients of each control unit by the current pollution generation amount of the corresponding control unit to generate multiple pollution load scenarios. The measurement of pollution load often requires a large amount of basic data support and calculation cost. By adding the random perturbation coefficients generated by Monte Carlo sampling, multiple groups of pollution generation scenarios can be quickly generated by effectively using a single current product amount calculation value, improving the simulation scenario setting efficiency while weakening randomness.

[0017] Further, in step S04, inputting the multiple pollution load scenarios into the river network model of the basin to obtain the water quality concentration of the target section includes:

[0018] Input multiple pollution load scenarios into the river network model of the basin to obtain the time series of water quality concentrations at the target section under the corresponding pollution load scenarios;

[0019] Take the average of the time series to obtain the water quality concentration at the target section for each control unit under the corresponding pollution load scenario.

[0020] Further, the value of the random perturbation coefficient in step S03 is within the range of [0, 1].

[0021] Further, in step S01, the control units are divided by combining hydrological unit division with administrative boundaries, including: conducting river network topology analysis, based on DEM (Digital Elevation Model) data, using hydrological analysis tools to generate river network vector data and spatial grid data, determining the confluence relationship of the river channels according to the flow direction and cumulative flow of each grid unit, and dividing sub-basins accordingly. Taking the river channels as boundaries, the river network area is divided into multiple hydrological response units (HRUs, Hydrological Response Units) through local correction of the sub-basins;

[0022] Combined with the nearby administrative boundaries, local adjustments are made to the hydrological response units to facilitate the subsequent implementation of pollution control measures from the administrative level, and finally multiple control units are generated.

[0023] In a second aspect, a river network water environment main control unit identification system based on Monte Carlo sampling is disclosed, including a control unit division module, a perturbation coefficient generation module, a water quality simulation module, a river network model of the basin, and a main control unit identification module,

[0024] The control unit division module is used to divide the river channels in the river network area into multiple control units;

[0025] The perturbation coefficient generation module is used to generate multiple random perturbation coefficients for each control unit through Monte Carlo sampling;

[0026] The water quality simulation module is used to perform water quality simulation for each control unit based on multiple random perturbation coefficients, and correspondingly generate multiple pollution load scenarios;

[0027] The river network model of the basin is used to obtain the water quality concentration at the target section under the corresponding pollution load scenario according to the multiple pollution load scenarios of each control unit;

[0028] The main control unit identification module is used to select water quality indicators to establish the response relationship between the random perturbation coefficient of each control unit and the water quality concentration at the target section, and determine the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units.

[0029] Further, the response relationship between the random perturbation coefficient of each control unit and the water quality concentration of the target section is a linear response relationship. The influence degree of each control unit on the water quality of the target section is determined according to the response relationship, and the main control units are identified as follows:

[0030] Calculate the correlation coefficient between the random perturbation coefficient of each control unit and the water quality concentration of the target section according to the linear response relationship between the multiple random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario. A large correlation coefficient corresponding to a control unit indicates a large influence degree of the control unit on the water quality of the target section.

[0031] Sort the correlation coefficients of all control units, and identify the control units with a large influence degree on the water quality of the target section as the main control units.

[0032] The beneficial effects produced by the present invention include: through sampling and simulation calculation, the present invention accurately quantifies the contribution degree of each control unit to the water quality of the key section, adopts the Monte Carlo sampling method to cover the uncertainty of pollution load, replaces the traditional fixed scenario analysis, reduces the dependence on measured data, improves the identification efficiency of the main control unit, and provides a scientific basis for water environment management and pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The following further specifically describes the present invention in conjunction with the drawings and specific embodiments, and the above and / or other advantages of the present invention will become clearer.

[0034] Figure 1 Flowchart of a method for identifying the main control unit of river network water environment based on Monte Carlo sampling provided by an embodiment of the present application;

[0035] Figure 2 Schematic diagram of the division of watershed control units of a method for identifying the main control unit of river network water environment based on Monte Carlo sampling provided by an embodiment of the present application;

[0036] Figure 3 Response curve of perturbation coefficient - water quality of target section of a method for identifying the main control unit of river network water environment based on Monte Carlo sampling provided by an embodiment of the present application.

[0037] Figure 4 Schematic diagram of the structure of a system for identifying the main control unit of river network water environment based on Monte Carlo sampling provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following will describe the embodiments of the present invention in conjunction with the drawings.

[0039] A method and system for identifying the main control units of river network water environment based on Monte Carlo sampling provided by this application are used to accurately identify the control units in the basin that have a significant impact on the water quality of key sections, providing a scientific basis for water environment management and pollution control.

[0040] As Figure 1 , the first embodiment of the present invention discloses a method for identifying the main control units of river network water environment based on Monte Carlo sampling, including the following steps:

[0041] Step S01, dividing the river network area into multiple control units; specifically, the control units are divided by combining hydrological unit division with administrative boundaries, including:

[0042] Using ArcGIS or MIKE Hydro to perform river system topology analysis, based on DEM data, using hydrological analysis tools to generate river network vector data and spatial grid data, determining the confluence relationship of the river according to the flow direction and cumulative confluence volume of each grid unit, and dividing the sub-basins accordingly. Taking the river as the boundary, the river network area is divided into multiple hydrological response units by locally correcting the sub-basins;

[0043] Combining administrative boundaries mainly involves locally adjusting the hydrological response units in combination with the nearby administrative boundaries to facilitate the implementation of pollution control measures from the administrative level in the follow-up, and finally generating M control units. .

[0044] Step S02, establishing a river network model for the basin;

[0045] The river network model for the basin can be a coupled model of hydrology, hydrodynamics and water quality, including integrating data such as precipitation, evaporation, land use, and basin zoning based on DEM data into data with the same resolution, completing the construction of hydrological and hydrodynamic modules, and establishing a water quality module based on the convection-diffusion equation.

[0046] Existing mature hydrodynamic and water quality modeling software (such as MIKE 11 or HEC-RAS) or self-developed programs can be used to establish a river network model for the basin.

[0047] Step S03, generating N random perturbation coefficients for each control unit through Monte Carlo sampling. ;

[0048] The value of the random perturbation coefficient is in the interval [0, 1]. The selection of the Monte Carlo sampling times N mainly affects the accuracy of establishing the response relationship. The larger N is, the more accurate the response relationship is, and the longer the calculation period is.

[0049] Step S04: Each control unit performs water quality simulation based on N random perturbation coefficients, and correspondingly generates N pollution load scenarios; input the N pollution load scenarios into the river network model of the basin to obtain the water quality concentration of the target section under the corresponding pollution load scenarios. Specifically, multiply the N random perturbation coefficients of each control unit by the current pollution generation amount of the corresponding control unit to generate N pollution load scenarios.

[0050] Input the N pollution load scenarios into the river network model of the basin to obtain the time series of the water quality concentration of the target section under the N pollution load scenarios;

[0051] Take the average value of the time series to obtain the water quality concentration of the target section of each control unit.

[0052] Step S05: Select water quality indicators to establish the response relationship between the N random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenarios, and determine the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units.

[0053] The selected water quality indicators include the water quality indicators that exceed the standard in the basin.

[0054] The response relationship between the N random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenarios is a linear response relationship. The formula is as follows:

[0055]

[0056] where represents the water quality concentration of the target section under the nth pollution load scenario obtained by the mth control unit, represents the nth random perturbation coefficient of the mth control unit, represents the correlation coefficient between the random perturbation coefficient of the mth control unit and the water quality concentration of the target section, represents the background value of the water quality concentration of the mth control unit; ; 。

[0057] Determining the influence degree of each control unit on the water quality of the target section according to the response relationship to identify the main control units includes:

[0058] Calculate the correlation coefficient between the random perturbation coefficient of each control unit and the water quality concentration of the target section according to the linear response relationship between the N random perturbation coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenarios , the larger the correlation coefficient corresponding to the control unit, the greater the influence degree of the control unit on the water quality of the target section;

[0059] Sort the correlation coefficients of all control units, and identify the control units with a large impact on the water quality of the target section as the main control units.

[0060] Embodiment:

[0061] This embodiment proposes a method for identifying the main control units of the water environment based on the simulation of the river network model in the Taihu Lake Basin and Monte Carlo sampling. By simulating the water quality changes in the basin under different pollution discharge scenarios of control units, the response relationship between the pollution load of control units and the water quality of key sections is established, so as to accurately identify the main control units, including the following steps:

[0062] Step S01 Divide units: Taking the West Lake Region of Taihu Lake (south to the Jiangsu-Zhejiang provincial boundary, Tianmu Mountain, west to Maoshan Mountain, north to the Yangtze River-Southern Jiangsu Canal, and east to Liangxi River-Taihu Lake) as an example, on the basis of the division results of the key basin catchment areas of the Ministry of Ecology and Environment, combined with the research scope and regional catchment characteristics, some catchment areas are locally adjusted to obtain 20 control units, as Figure 2 shown.

[0063] S02 Establish a model: Establish a hydrological-hydrodynamic-water quality coupling model for simulating the water volume of rivers and lakes in the Taihu Lake Basin. Based on the 50m DEM data, integrate data such as precipitation, evaporation, land use, and basin zoning into data with the same resolution, complete the construction of the hydrological-hydrodynamic module, and establish a water quality module based on the convection-diffusion equation.

[0064] S03 Coefficient sampling: For a certain control unit, use Monte Carlo sampling to generate 100 groups of random numbers between 0 and 1 as perturbation coefficients.

[0065] S04 Water quality simulation: Multiply the perturbation coefficient by the current pollution discharge amount inside the control unit to obtain 100 groups of pollution discharge scenarios, and substitute them into the river network simulation of the Taihu Lake Basin for calculation to obtain the water quality concentration field of the basin under the corresponding pollution discharge scenarios. There are 20 control units in this case, and each control unit is perturbed 100 times, resulting in a total of 2000 groups of calculation scenarios;

[0066] S05 Main control identification: Taking the Yincungangqiao section as the target section, select the control units where the Baidugangqiao, Bieqiao, North of Gehu Lake, and South of Gehu Lake sections are located as comparison objects, select COD (Chemical Oxygen Demand) as the main water quality index, and establish a linear response relationship between the perturbation coefficient and the water quality concentration of the target section, as Figure 3As shown, the influence degree of each control unit on the water quality of the target section is ranked according to the correlation coefficient. The calculation results show that the correlation coefficient between the disturbance coefficient of the Baidugangqiao control unit and the COD concentration of the Yincungangqiao section is 0.01, the correlation coefficient between the disturbance coefficient of the Bieqiao control unit and the COD concentration of the Yincungangqiao section is 0.1, the correlation coefficient between the disturbance coefficient of the North Gehu control unit and the COD concentration of the Yincungangqiao section is 0.04, and the correlation coefficient between the disturbance coefficient of the South Gehu control unit and the COD concentration of the Yincungangqiao section is 0.8. Among the four control units, the South Gehu control unit has the greatest influence on the COD of the Yincungangqiao section, followed by the Bieqiao control unit. Therefore, the key control units of the Yincungangqiao section are identified as the South Gehu control unit and the Bieqiao control unit.

[0067] As Figure 4 shown, the second embodiment of the present invention discloses a river network water environment main control unit identification system based on Monte Carlo sampling, including a control unit division module, a disturbance coefficient generation module, a water quality simulation module, a basin river network model, and a main control unit identification module.

[0068] The control unit division module is used to divide the river channels in the river network area into multiple control units.

[0069] The disturbance coefficient generation module is used to generate multiple random disturbance coefficients for each control unit through Monte Carlo sampling.

[0070] The water quality simulation module is used to perform water quality simulation for each control unit based on multiple random disturbance coefficients, and correspondingly generate multiple pollution load scenarios.

[0071] The basin river network model is used to obtain the water quality concentration of the target section according to the multiple pollution load scenarios of each control unit.

[0072] The main control unit identification module is used to select water quality indicators to establish the response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section, and determine the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units.

[0073] In this embodiment, the response relationship between the random disturbance coefficient of each control unit and the water quality concentration of the target section is a linear response relationship. Determining the influence degree of each control unit on the water quality of the target section according to the response relationship, so as to identify the main control units includes:

[0074] Calculating the correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section according to the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario. A large correlation coefficient corresponding to the control unit indicates a large influence degree of the control unit on the water quality of the target section.

[0075] Rank the correlation coefficients of all control units, and identify the control units with a greater impact on the water quality of the target section as the main control units.

[0076] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit. Among them, the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, it can run the inventive content of a method for identifying the main control unit of the river network water environment based on Monte Carlo sampling and some or all of the steps in each embodiment. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0077] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general hardware platform. Based on such an understanding, the essence of the technical solutions in the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a computer program, that is, a software product. The computer program software product can be stored in a storage medium, including several instructions for causing a device (which can be a personal computer, a server, a single-chip microcomputer, a MUU, or a network device, etc.) including a data processing unit to execute the methods described in each embodiment or some parts of the embodiments of the present invention.

[0078] The present invention provides a method and a system for identifying the main control unit of the river network water environment based on Monte Carlo sampling. There are many methods and ways to specifically implement this technical solution. The above is only the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by the prior art.

Claims

1. A method for identifying the main control unit of river network water environment based on Monte Carlo sampling, characterized in that: The following steps are involved: Step S01, dividing the river network area into multiple control units; Step S02, establishing a river network model of a watershed; Step S03, generating a plurality of random disturbance coefficients for each control unit through Monte Carlo sampling; Step S04, each control unit performs water quality simulation based on multiple random disturbance coefficients, and generates multiple pollution load scenarios accordingly; the multiple pollution load scenarios are input into the river network model of the watershed to obtain the water quality concentration of the target section under the corresponding pollution load scenario; Step S05, select water quality indicators to establish a response relationship between multiple random disturbance coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario, determine the degree of influence of each control unit on the water quality of the target section based on the response relationship, and thus identify the main control unit.

2. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 1 is characterized in that: The response relationship between the multiple random disturbance coefficients of each control unit in the step S05 and the water quality concentration of the target section under the corresponding pollution load scenario is a linear response relationship.

3. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 2 is characterized in that: Determining the influence of each control unit on the target section water quality according to the response relationship in step S05, thereby identifying the main control unit includes: calculating the correlation coefficient between the random disturbance coefficient of each control unit and the target section water quality concentration according to the linear response relationship between the multiple random disturbance coefficients of each control unit and the target section water quality concentration under the corresponding pollution load scenario, and a large correlation coefficient corresponding to the control unit indicates that the control unit has a large influence on the target section water quality; The correlation coefficients of all control units are ranked, and the control unit with the greatest impact on the water quality of the target section is identified as the main control unit.

4. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 3 is characterized in that: The step S05 of selecting water quality indicators includes selecting water quality indicators that exceed the standard in the watershed.

5. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 4 is characterized in that: In step S04, each control unit performs water quality simulation based on multiple random disturbance coefficients, and correspondingly generates multiple pollution load scenarios, including: multiplying the multiple random disturbance coefficients of each control unit with the current pollution production of the corresponding control unit to generate multiple pollution load scenarios.

6. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 5 is characterized in that: In step S04, the multiple pollution load scenarios are input into the river network model of the watershed to obtain the target section water quality concentration under the corresponding pollution load scenario, including: inputting multiple pollution load scenarios into the river network model of the watershed to obtain the time series of the target section water quality concentration under the corresponding pollution load scenario; The time series is averaged to obtain the target section water quality concentration under the pollution load scenario corresponding to each control unit.

7. The method for identifying the main control unit of the river network water environment based on Monte Carlo sampling according to claim 6 is characterized in that: The random disturbance coefficient in step S03 has a value in the interval [0,1].

8. The method for identifying the main control unit of river network water environment based on Monte Carlo sampling according to claim 7 is characterized in that: The step S01 divides the control unit by combining hydrological unit division with administrative management boundaries, including: performing water system topology analysis, generating river network vector data and spatial grid data based on DEM data using hydrological analysis tools, determining the confluence relationship of the river channel according to the flow direction and confluence accumulation of each grid unit, and dividing the sub-basin accordingly, dividing the river network area into multiple hydrological response units by locally correcting the sub-basin with the river channel as the boundary; The hydrological response units are locally adjusted in combination with the nearest administrative boundaries, and multiple control units are eventually generated.

9. A river network water environment main control unit identification system based on Monte Carlo sampling, characterized in that: It includes control unit division module, disturbance coefficient generation module, water quality simulation module, river basin network model and main control unit identification module. The control unit division module is used to divide the river channels in the river network area into multiple control units; The disturbance coefficient generation module is used to generate a plurality of random disturbance coefficients for each control unit through Monte Carlo sampling; The water quality simulation module is used for each control unit to perform water quality simulation based on multiple random disturbance coefficients, and correspondingly generate multiple pollution load scenarios; The river network model of the watershed is used to obtain the water quality concentration of the target section under the corresponding pollution load scenario according to the multiple pollution load scenarios of each control unit; The main control unit identification module is used to select water quality indicators to establish a response relationship between the random disturbance coefficient of each control unit and the water quality concentration of the target section, and determine the degree of influence of each control unit on the water quality of the target section according to the response relationship, thereby identifying the main control unit.

10. A river network water environment main control unit identification system based on Monte Carlo sampling according to claim 9, characterized in that: The response relationship between the random disturbance coefficient of each control unit and the water quality concentration of the target section is a linear response relationship. The influence of each control unit on the water quality of the target section is determined according to the response relationship, so that the main control units are identified, including: The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section is calculated according to the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario. The larger the correlation coefficient corresponding to the control unit, the greater the influence of the control unit on the water quality of the target section. The correlation coefficients of all control units are ranked, and the control unit with the greatest impact on the water quality of the target section is identified as the main control unit.

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