Method and system for identifying main control units of river network water environment based on Monte Carlo sampling

Abstract

The present invention provides a method and system for identifying the primary control units of a river network water environment based on Monte Carlo sampling. The method comprises step S01: dividing a river channel in a river network area into multiple control units; step S02: establishing a river basin network model; step S03: generating multiple random perturbation coefficients for each control unit through Monte Carlo sampling; step S04: performing water quality simulations for each control unit based on the multiple random perturbation coefficients, correspondingly generating multiple pollution load scenarios; inputting the multiple pollution load scenarios into the river basin network model to obtain target section water quality concentrations; step S05: selecting water quality indicators to establish a response relationship between the multiple random perturbation coefficients of each control unit and the target section water quality concentration; and determining the degree of influence of each control unit on the target section water quality based on the response relationship, thereby identifying the primary control unit. This method can provide guidance for accurately identifying primary control units in the process of river basin environmental governance.

Description

Technical Field

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

[0002] In water environment management, identifying the primary control units that significantly impact water quality at key sections is crucial for developing effective pollution control strategies. However, existing technologies often struggle to accurately quantify the contribution of each control unit to water quality, and lack systematic analytical methods, resulting in ineffective and ineffective pollution control measures. Summary of the Invention

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide a method and system for identifying the main control units of the river network water environment based on Monte Carlo sampling, so as to accurately identify the control units that have a significant impact on the water quality of key sections in the basin, and provide a scientific basis for water environment management and pollution control.

[0004] In order to solve the above technical problems, in a first aspect, a method for identifying a river network water environment master control unit based on Monte Carlo sampling is disclosed, comprising the following steps:

[0005] Step S01, dividing the river network area into multiple control units;

[0006] Step S02, establishing a river network model of the watershed;

[0007] Step S03, generating a plurality of random perturbation coefficients for each control unit through Monte Carlo sampling;

[0008] In step S04, each control unit performs water quality simulation based on multiple random disturbance coefficients to generate multiple pollution load scenarios; the multiple pollution load scenarios are input into the river network model to obtain the water quality concentration of the target section under the corresponding pollution load scenario;

[0009] 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, and determine the degree of influence of each control unit on the water quality of the target section based on the response relationship, so as to identify the main control unit.

[0010] Furthermore, in step S05, the response relationship between the multiple random perturbation coefficients of each control unit and the target section water quality concentration under the corresponding pollution load scenario is a linear response relationship. Establishing a linear response relationship allows for direct mathematical quantification of the correlation between the perturbation coefficient and the target section water quality concentration. Furthermore, by characterizing the correlation coefficient through a slope value, the degree of correlation can be directly visualized, simplifying the decision-making process. Furthermore, it simplifies the mechanistic correlation of complex water environment systems, reducing computational costs.

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

[0012] Furthermore, in step S05, the influence degree of each control unit on the water quality of the target section is determined based on the response relationship, thereby identifying the main control units, including:

[0013] The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario is calculated based on the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section. The larger the correlation coefficient corresponding to the control unit, the greater the impact of the control unit on the water quality of the target section.

[0014] 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.

[0015] Furthermore, the step S05 of selecting water quality indicators includes selecting water quality indicators that exceed the standard in the watershed. This method is mainly used to formulate regional water environment control strategies. Selecting water quality indicators that exceed the standard in the watershed from a large number of water quality indicators is more targeted and practical.

[0016] Furthermore, in step S04, each control unit performs water quality simulation based on multiple random perturbation coefficients, and the corresponding generation of multiple pollution load scenarios includes: multiplying the multiple random perturbation coefficients of each control unit by the current pollution production 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 computational cost. By adding random perturbation coefficients generated by Monte Carlo sampling, it is possible to effectively utilize the current product volume calculation value to quickly generate multiple sets of pollution production scenarios, reducing randomness while improving the efficiency of simulation scenario setting.

[0017] Furthermore, in step S04, multiple pollution load scenarios are input into the river network model to obtain the target section water quality concentration, including:

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

[0019] The time series is averaged to obtain the target section water quality concentration under the pollution load scenario corresponding to each control unit.

[0020] Furthermore, the random perturbation coefficient in step S03 is within the interval [0, 1].

[0021] Furthermore, step S01 divides the control unit by combining hydrological unit division with administrative boundaries, including: performing water system topology analysis, generating river network vector data and spatial grid data based on DEM (Digital Elevation Model) data using hydrological analysis tools, determining the confluence relationship of the river channel according to the flow direction and cumulative flow of each grid cell, and dividing the sub-basin accordingly, dividing the river network area into multiple hydrological response units (HRUs) by locally correcting the sub-basin with the river channel as the boundary;

[0022] Local adjustments to the hydrological response units are made in combination with the nearest administrative boundaries to facilitate the subsequent implementation of pollution control measures at the administrative level, ultimately generating multiple control units.

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

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

[0025] The disturbance coefficient generation module is used to generate a plurality of random disturbance coefficients for each control unit through Monte Carlo sampling;

[0026] 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;

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

[0028] 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 based on the response relationship, thereby identifying the main control unit.

[0029] Furthermore, 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 degree of influence of each control unit on the water quality of the target section is determined according to the response relationship, thereby identifying the main control units, including:

[0030] The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario is calculated based on the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section. The larger the correlation coefficient corresponding to the control unit, the greater the impact of the control unit on the water quality of the target section.

[0031] 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.

[0032] The beneficial effects produced by the present invention include: the present invention accurately quantifies the contribution of each control unit to the water quality of key sections through sampling and simulation calculations, 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 recognition 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 present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0034] Figure 1 A flow chart of a method for identifying a river network water environment master control unit based on Monte Carlo sampling provided in an embodiment of the present application;

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

[0036] Figure 3 The embodiment of the present application provides a method for identifying a main control unit of a river network water environment based on Monte Carlo sampling and a disturbance coefficient-target section water quality response curve.

[0037] Figure 4 A schematic structural diagram of a river network water environment main control unit identification system based on Monte Carlo sampling is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The embodiments of the present invention will be described below with reference to the accompanying drawings.

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

[0040] like Figure 1 The first embodiment of the present invention discloses a method for identifying a river network water environment master control unit based on Monte Carlo sampling, comprising the following steps:

[0041] Step S01: Divide the river network into multiple control units. Specifically, the control unit division is performed by combining hydrological unit division with administrative boundaries, including:

[0042] Use ArcGIS or MIKE Hydro to perform water system topology analysis. Based on DEM data, use hydrological analysis tools to generate river network vector data and spatial grid data. Determine the confluence relationship of the river channel based on the flow direction and cumulative flow of each grid cell, and divide the sub-basin accordingly. Using the river channel as the boundary, locally modify the sub-basin to divide the river network area into multiple hydrological response units.

[0043] Combining administrative boundaries mainly involves local adjustments to the hydrological response units based on the nearest administrative boundaries, facilitating the subsequent implementation of pollution control measures at the administrative level, and ultimately generating M control units. .

[0044] Step S02, establishing a river network model of the watershed;

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

[0046] The river network model of the basin can be established using existing mature hydrodynamic and water quality modeling software (such as MIKE 11 or HEC-RAS) or self-developed programs.

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

[0048] The random perturbation coefficient is in the range [0,1]. The selection of the number of Monte Carlo sampling N mainly affects the accuracy of the response relationship. The larger N is, the more accurate the response relationship is, but the calculation period is also longer.

[0049] Step S04: Each control unit performs water quality simulation based on N random disturbance coefficients, and generates N pollution load scenarios accordingly; inputs 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 scenario; specifically, the N random disturbance coefficients of each control unit are compared with the current pollution output of the corresponding control unit. Multiply them together to generate N pollution load scenarios.

[0050] Inputting N pollution load scenarios into the river network model of the watershed to obtain a time series of water quality concentrations at the target section under the N pollution load scenarios;

[0051] The time series is averaged to obtain the water quality concentration of the target section of each control unit.

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

[0053] The selection of water quality indicators includes selecting water quality indicators that exceed the standards in the watershed.

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

[0055]

[0056] in, 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 disturbance coefficient of the mth control unit and the water quality concentration of the target section, represents the background value of water quality concentration in the mth control unit; ; .

[0057] According to the response relationship, the influence of each control unit on the water quality of the target section is determined, so that the main control units are identified, including:

[0058] The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section is calculated based on the linear response relationship between the N random disturbance coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario. , the correlation coefficient corresponding to the control unit The larger the value, the greater the impact of the control unit on the water quality of the target section;

[0059] 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.

[0060] Example:

[0061] This embodiment proposes a method for identifying master control units in the water environment based on a Taihu Lake basin river network model simulation and Monte Carlo sampling. By simulating changes in water quality in the basin under different pollution-producing control units, a relationship between the pollution load of the control unit and the water quality response of key sections is established, thereby accurately identifying the master control unit. The method includes the following steps:

[0062] Step S01: Unit division: Taking the western area of Taihu Lake (south to the boundary between Jiangsu and Zhejiang provinces and Tianmu Mountain, west to Maoshan Mountain, north to the Yangtze River-South Jiangsu Canal, east to Liangxi River-Taihu Lake) as an example, based on the results of the watershed division of key basins of the Ministry of Ecology and Environment, combined with the research scope and regional watershed characteristics, some watershed areas were partially adjusted to obtain 20 control units, such as Figure 2 shown.

[0063] S02 Model Building: Build a hydrological-hydrodynamic-water quality coupling model for simulating river and lake water volume in the Taihu Lake Basin. Based on 50m DEM data, integrate precipitation, evaporation, land use, watershed zoning and other information into data of 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, Monte Carlo sampling is used to generate 100 groups of random numbers between 0 and 1 as disturbance coefficients.

[0065] S04 Water Quality Simulation: Multiply the disturbance coefficient by the current pollution output within the control unit to obtain 100 pollution scenarios. These scenarios are then applied to the Taihu Lake Basin river network simulation to calculate the water quality concentration field under the corresponding pollution scenarios. This case study has 20 control units, each of which is disturbed 100 times, generating a total of 2,000 calculation scenarios.

[0066] S05 Master Control Identification: Take the Yincun Port Bridge section as the target section, select the control units of the Baidu Port Bridge, Bieqiao, North Gehu Lake, and South Gehu Lake sections as comparison objects, select COD (Chemical Oxygen Demand) as the main water quality indicator, and establish a linear response relationship between the disturbance coefficient and the water quality concentration of the target section, such as Figure 3As shown. The degree of influence 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 Baidugang Bridge control unit and the COD concentration of the Yincungang Bridge section is 0.01, the correlation coefficient between the disturbance coefficient of the Bieqiao control unit and the COD concentration of the Yincungang Bridge section is 0.1, the correlation coefficient between the disturbance coefficient of the Gehu North control unit and the COD concentration of the Yincungang Bridge section is 0.04, and the correlation coefficient between the disturbance coefficient of the Gehu South control unit and the COD concentration of the Yincungang Bridge section is 0.8. Among the four control units, the Gehu South control unit has the greatest impact on the COD of the Yincungang Bridge section, followed by the Bieqiao control unit. Therefore, the key control units of the Yincungang Bridge section are identified as the Gehu South control unit and the Bieqiao control unit.

[0067] like Figure 4 As 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 watershed river network model and a main control unit identification module.

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

[0069] The disturbance coefficient generation module is used to generate a plurality of random disturbance coefficients for each control unit through Monte Carlo sampling;

[0070] 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;

[0071] The watershed river network model is used to obtain target section water quality concentrations based on multiple pollution load scenarios for each control unit;

[0072] The main control unit identification module is used to 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, and determine the degree of influence of each control unit on the water quality of the target section based on the response relationship, thereby identifying the main control unit.

[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. The degree of influence of each control unit on the water quality of the target section is determined based on the response relationship, thereby identifying the main control units, including:

[0074] The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario is calculated based on the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section. The larger the correlation coefficient corresponding to the control unit, the greater the impact of the control unit on the water quality of the target section.

[0075] 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.

[0076] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program that, when executed by the data processing unit, executes the invention disclosure of a method for identifying a river network water environment master control unit based on Monte Carlo sampling, as well as some or all of the steps in various embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0077] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a computer program, i.e., a software product. This computer program software product can be stored in a storage medium and includes a number of instructions for enabling a device including a data processing unit (such as a personal computer, server, single-chip microcomputer, MUU, or network device) to execute the methods described in various embodiments of the present invention or certain portions of these embodiments.

[0078] The present invention provides a method and system for identifying master control units in river water environments based on Monte Carlo sampling. While there are numerous methods and approaches for implementing this technical solution, the foregoing description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Any components not specified in this embodiment may be implemented using existing technologies.

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 the watershed; Step S03, generating a plurality of random perturbation coefficients for each control unit through Monte Carlo sampling; Step S04: Each control unit performs water quality simulation based on multiple random disturbance coefficients to generate multiple pollution load scenarios, including: multiplying the multiple random disturbance coefficients of each control unit by the current pollution production of the corresponding control unit to generate multiple pollution load scenarios; Inputting multiple pollution load scenarios 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 linear 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. The formula is as follows: ， in, 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 disturbance coefficient of the mth control unit and the water quality concentration of the target section, represents the background value of water quality concentration in the mth control unit; ; , M represents the number of control units, and N represents the number of random perturbation coefficients generated by each control unit; Determining 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, including: 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 multiple random disturbance coefficients of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario, wherein a larger correlation coefficient corresponding to a control unit indicates that the control unit has a greater degree of influence 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.

2. The method for identifying the main control unit of river network water environment based on Monte Carlo sampling according to claim 1, characterized in that: The step S05 of selecting water quality indicators includes selecting water quality indicators that exceed the standard in the watershed.

3. The method for identifying the main control unit of river network water environment based on Monte Carlo sampling according to claim 2, characterized in that: In step S04, multiple pollution load scenarios are input into the river network model 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 a time series of water quality concentrations at the target section under the corresponding pollution load scenarios; The time series is averaged to obtain the target section water quality concentration under the pollution load scenario corresponding to each control unit.

4. The method for identifying the main control unit of river network water environment based on Monte Carlo sampling according to claim 3 is characterized in that: The random perturbation coefficient in step S03 is within the interval [0, 1].

5. The method for identifying the main control unit of river network water environment based on Monte Carlo sampling according to claim 4 is characterized in that: Step S01 divides the control unit by combining hydrological unit division with administrative 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 cell, and dividing the sub-basin accordingly, dividing the river network area into multiple hydrological response units by locally modifying 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.

6. A river network water environment master 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 channel 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 to generate multiple pollution load scenarios, including: multiplying the multiple random disturbance coefficients of each control unit by the current pollution production of the corresponding control unit to generate multiple pollution load scenarios; The river network model is used to obtain the water quality concentration of the target section under the corresponding pollution load scenario based on multiple pollution load scenarios of each control unit; The main control unit identification module is used to select water quality indicators to establish a linear response relationship between the random disturbance coefficient of each control unit and the water quality concentration of the target section. The formula is as follows: ， in, 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 disturbance coefficient of the mth control unit and the water quality concentration of the target section, represents the background value of water quality concentration in the mth control unit; ; , M represents the number of control units, and N represents the number of random perturbation coefficients generated by each control unit; The main control unit identification module determines the degree of influence of each control unit on the water quality of the target section based on the response relationship, thereby identifying the main control unit, including: The correlation coefficient between the random disturbance coefficient of each control unit and the water quality concentration of the target section under the corresponding pollution load scenario is calculated based on the linear response relationship between the multiple random disturbance coefficients of each control unit and the water quality concentration of the target section. The larger the correlation coefficient corresponding to the control unit, the greater the impact 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.

Images