CN115188434A - Watershed-scale water body non-point source pollution classification and partition identification method - Google Patents
Watershed-scale water body non-point source pollution classification and partition identification method Download PDFInfo
- Publication number
- CN115188434A CN115188434A CN202210527218.7A CN202210527218A CN115188434A CN 115188434 A CN115188434 A CN 115188434A CN 202210527218 A CN202210527218 A CN 202210527218A CN 115188434 A CN115188434 A CN 115188434A
- Authority
- CN
- China
- Prior art keywords
- point source
- load
- data
- flow
- source pollution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000005192 partition Methods 0.000 title claims abstract description 25
- 238000004458 analytical method Methods 0.000 claims abstract description 24
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 239000003344 environmental pollutant Substances 0.000 claims description 40
- 231100000719 pollutant Toxicity 0.000 claims description 40
- 238000011160 research Methods 0.000 claims description 23
- 238000005457 optimization Methods 0.000 claims description 17
- 230000011218 segmentation Effects 0.000 claims description 17
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 230000002068 genetic effect Effects 0.000 claims description 11
- 238000011002 quantification Methods 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 11
- 238000007619 statistical method Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 9
- 238000001704 evaporation Methods 0.000 claims description 9
- 230000008020 evaporation Effects 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 7
- 238000013179 statistical model Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 5
- 239000011435 rock Substances 0.000 claims description 5
- 230000001932 seasonal effect Effects 0.000 claims description 5
- 238000010586 diagram Methods 0.000 claims description 4
- 238000003911 water pollution Methods 0.000 claims description 4
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 3
- 230000004907 flux Effects 0.000 claims description 3
- 230000005251 gamma ray Effects 0.000 claims description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 2
- 230000002301 combined effect Effects 0.000 claims 1
- 238000012790 confirmation Methods 0.000 abstract description 2
- 230000002349 favourable effect Effects 0.000 abstract 1
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 12
- 229910052757 nitrogen Inorganic materials 0.000 description 7
- 230000008859 change Effects 0.000 description 3
- 238000012271 agricultural production Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000010865 sewage Substances 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 239000002352 surface water Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 101100326726 Flaveria trinervia PPCC gene Proteins 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 239000004698 Polyethylene Substances 0.000 description 1
- 238000006065 biodegradation reaction Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000012851 eutrophication Methods 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000010842 industrial wastewater Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- -1 polyethylene Polymers 0.000 description 1
- 229920000573 polyethylene Polymers 0.000 description 1
- USHAGKDGDHPEEY-UHFFFAOYSA-L potassium persulfate Chemical compound [K+].[K+].[O-]S(=O)(=O)OOS([O-])(=O)=O USHAGKDGDHPEEY-UHFFFAOYSA-L 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 238000002798 spectrophotometry method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Business, Economics & Management (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- General Engineering & Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- General Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Computational Linguistics (AREA)
- Medical Informatics (AREA)
- Economics (AREA)
- Fuzzy Systems (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mathematical Physics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- Artificial Intelligence (AREA)
Abstract
The invention discloses a classification and partition identification method for non-point source pollution of a watershed scale water body, which comprises the following steps of 1: obtaining basic data and quantifying non-point source pollution load; and 2, step: dividing and quantifying the basic flow and dividing and quantifying the basic flow load; and step 3: obtaining load data of a required region, a land utilization type and a unit area of a certain land utilization type; and 4, step 4: constructing a non-point source pollution source analysis model; and 5: data are obtained through the step 1, and parameter calibration of the non-point source pollution source analysis model is completed; step 6: verifying and evaluating the source analysis model result: dividing the data in the step 1 into two parts according to a time sequence; and 7: classifying and identifying non-point source pollution sources; and step 8: and identifying the non-point source pollution source partition. The method further optimizes the output coefficient model, realizes more scientific, reasonable and accurate classification and partition quantitative source analysis of the watershed scale non-point source pollution, is favorable for better source monitoring and responsibility confirmation, and effectively reduces the pollution.
Description
Technical Field
The invention relates to the technical field of sewage control and treatment, in particular to a classification and partition identification method for non-point source pollution of a watershed scale water body.
Background
With the continuous improvement of the control and treatment level of point source pollution such as industrial wastewater, municipal sewage and the like, non-point source pollution caused by agricultural production, water and soil loss, atmospheric sedimentation and the like becomes a primary pollution source of water environment. As for the contribution of different types of non-point source pollution to the degradation of water environment quality, the contribution of agricultural non-point source pollution is the largest, so that the non-point source pollution of the surface water environment caused by agricultural production activities is always a focus of attention in water pollution research at home and abroad. The non-point source pollution has the characteristics of complex components, various types, intermittent generation mode, uncertain generation time, various emission and discharge modes, difficulty in determination, rapid time-space change and the like. Due to these characteristics, monitoring and control of non-point source contamination is made more difficult.
Base flow generally refers to the portion of the runoff in a river derived from groundwater or other lag water sources. Long-term agricultural intensification not only causes the increase of the concentration of nitrogen (N) and phosphorus (P) in surface water bodies, but also causes serious eutrophication tendency of underground water in various regions, so that the base flow becomes an important way for outputting non-point source N and P pollutants in agricultural watershed. However, the existing method for quantitatively tracing the non-point source pollution in the drainage basin generally attributes all pollution loads to surface runoff, and seriously misleads scientific understanding and subsequent working strategies of non-point source pollution treatment in the drainage basin; in addition, the traditional output coefficient method has the defects (such as lack of consideration of pollutant migration loss, biodegradation and the like), so that the classification and partition identification of the drainage basin non-point source pollution based on the traditional method has larger uncertainty.
Disclosure of Invention
1. Technical problem to be solved
The invention aims to provide a more scientific and reasonable classification and partition identification method for non-point source pollution of a water body by optimizing and improving a traditional output coefficient model on the premise of fully considering the contribution of non-point source pollution of a base flow by basin scale.
2. Technical scheme
In order to achieve the purpose, the invention adopts the following technical scheme:
a classification and partition identification method for non-point source pollution of a watershed scale water body comprises the following steps:
step 1: obtaining basic data and quantifying non-point source pollution load; the basic data acquisition comprises hydrological (flow) data and water quality (pollutant concentration) data at the outlet of a given basin, meteorological data of the whole basin and land utilization data; the non-point source pollution load quantification is based on discrete hydrological (flow) and water quality (pollutant concentration) data obtained by monitoring, and the daily runoff load at the outlet of the watershed is calculated by utilizing a LOADEST model developed by the American geological survey bureau:
in the formula (I), the compound is shown in the specification,
the method is characterized in that the total runoff load (kg. D) of the instantaneous river channel calculated by a corrected maximum likelihood estimation method is used -1 )；a 0 ～a j Parameters of a pollutant flux regression equation; h (a, b, s) 2 α, k) is a likelihood approximation function of infinite series; a and b are functions that interpret variables; alpha and kappa are functions of gamma distribution; m is a degree of freedom; s 2 Is the residual error.
Step 2: dividing and quantifying the basic flow and the load thereof;
the basic flow segmentation quantification of the current domain adopts two-parameter recursive filtering (ERDF) proposed by Eckhardt in 2005:
in the formula, q t 、Q t Respectively the total runoff and the base runoff at the time t; Δ t is the time step (days); alpha is a water-discharge constant (0) reflecting the water-discharge rate of the river<α<1)；BFI max Is the maximum base flow index. Due to BFI max It cannot be determined directly, eckhardt (2005) gives three empirical values: 0.80, 0.50 and 0.25, corresponding to the perennial rivers and seasonal rivers of the porous medium aquifer region and the perennial rivers of the hard rock medium aquifer region, respectively.
On the basis of realizing the basic flow segmentation quantification, a regression statistical model of basic flow load regression coefficients is constructed on the basis of hydrological and meteorological factors closely related to the basic flow load regression process, see formula 3, for estimating the basic flow load regression coefficients day by day, after the load regression coefficients are determined, the regression statistical model is back-substituted into a recursive digital filtering equation, see formula 2, so that the daily load quantity obtained by the LOADEST model simulation can be segmented to obtain the basic flow load quantity, the basic flow load quantification segmentation method is called recursive filtering basic flow load segmentation algorithm (RFLSA) for short,
τ(t)＝γ 1 ×a(t)+γ 2 ×P(t)+γ 3 ×E(t)+C (3)
wherein tau is a base stream load shedding parameter; a is a water withdrawal parameter; p and E are rainfall and evaporation respectively; t is time (in days); gamma ray 1 ～γ 3 Is a fitting parameter;
and step 3: obtaining load data of a required area, land utilization types, unit areas of certain land utilization types (paddy fields, forest lands and the like), regional meteorological data, pollutant data of the atmosphere which is settled into a river water body, and pollutants generated by point source pollution;
and 4, step 4: constructing a non-point source pollution source analysis model:
in the formula, L is the river entering amount of non-point source pollutants; BL is base stream non-pointSource pollution load; x i Discharge amount of certain pollutant per unit area for certain period of i-th land utilization type (e.g. 1 day, 1 week), S i Is X i The correction parameter of (2) can be expressed as a function of the meteorological factor; w (t) is the ratio of the surface runoff in a certain period of time to the average surface runoff in a research period of time; m (t) is a pollutant which is precipitated into a river water body by the air; d (t) is a pollutant generated by point source pollution; n is the number of land utilization types; t is time, generally days or weeks, P is rainfall data, E is evaporation data, T is average temperature data, R is relative humidity data, all are meteorological data, η represents the comprehensive influence of all factors except the factors contained in the function, and θ 1 ～θ 4 Solving the obtained equation fitting parameters for numerical optimization;
and 5: data are obtained through the step 1, and parameter calibration of the non-point source pollution source analysis model is completed;
and 6: calibration and verification: the data in step 1 is divided into two parts according to time sequence, the former part is used for determining and calibrating the model parameters in step 2, the latter part is used for verifying and evaluating the model simulation result, and the selected evaluation indexes are NSE, root mean square error-measured value standard deviation ratio (RSR) and decision coefficient (R) 2 ) The specific calculation method is as follows:
in the formula, L (i,m) And L (i,s) Respectively representing an actual measurement value and an analog value of the load capacity of the ith polluted land; l is avg An average value of measured values representing the pollutant load; n is the measured valueCounting;
the measured value can be obtained by calculating based on the instantaneous flow Q of the water flow and corresponding pollutant concentration C continuous data under the flow condition through the L = KCQ formula, and the analog value is obtained by a classification and partition identification formula;
in the formula of L = KCQ, L is load; k is a unit conversion coefficient; c concentration, Q is river flow, the formula is an estimation formula of water pollution load, the pollution load is the total amount of pollutants entering the water body from a point pollution source and a surface pollution source in a certain period of time;
and 7: classifying and identifying the non-point source pollution source, and finishing the statistics and summary of various land utilization areas in the region by using ARCGIS software and a corresponding land utilization map; and (4) calculating to obtain the non-point source pollution discharge amount of the surface runoff in the given time period of different land types of the drainage basin by combining the output coefficient model of the output perimeter scale after parameter optimization and verification are completed.
And 8: and identifying non-point source pollution source partitions, performing statistical analysis on various data in the flow field by using the statistical analysis function of ARCGIS software, dividing the research area into required grades through national villages and towns partitions, and substituting the land areas of each administrative area and the meteorological data in a required time period by using the obtained week scale output coefficient model to respectively obtain the non-point source pollution discharge amount of the surface runoff in a given time period of each administrative area block in the research area.
Preferably, the hydrological and meteorological data day by day in the step 1 are provided by government related departments; the water quality data is obtained by regular monitoring, the analysis and determination method of specific indexes refers to national relevant standards, and according to the 2017 latest edition 'classification of the current state of land utilization' (GBT 21010-2017), the classification statistics of various land utilizations of a given basin is completed by using ARCGIS software and remote sensing images.
Preferably, the BFI in the step 2 max Cannot be obtained by direct measurement at present, BFI max The value of (A) is obtained by calculating and simulating by using a mathematical algorithm on the basis of combining with the hydrogeological characteristics of the drainage basin: perennial rivers and seasonal rivers in porous medium aquifer regions and in hard rock medium aquifer regionsBFI of perennial river max Suggested values are 0.80, 0.50 and 0.25, respectively.
Preferably, the method for determining the ERDF parameter comprises the following steps:
s1, according to an empirical formula, N =0.83A 0.2 (N is the number of days required for the surface runoff to completely stop after the flood peak; A is the drainage basin area in km 2 ) Determining a starting point of pure base flow water withdrawal;
s2, the condition y is satisfied 1 >y 2 >…>y k >y k+1 >y k+2 Flow data y of k And y k+1 Screening out the daily flow sequence to obtain a pure base flow water withdrawal process; the minimum length of the selected water return process is 5 days, and in areas with high rainfall occurrence frequency, the interference of rainfall needs to be eliminated as much as possible during water return analysis;
s3, fitting a scatter diagram (y) by using a linear equation passing through the origin k vs y k+1 ) The slope of the equation obtained from the upper boundary point of (1) is the water-withdrawal constant of the ERDF;
s4, calculating the average water-removing constant and the monthly scale water-removing constant of the whole process according to the method;
s5, in order to reduce the variability of the base flow recession rule in the different base flow recession processes and the uncertainty of the base flow segmentation result caused by the empirical value of the parameter BFImax, a first-order Fourier fitting function of the daily recession constant is obtained on the basis of the monthly recession constant; then, on the basis of the function, a calculation equation of the daily water-withdrawal constant of the ERDF is constructed, see a formula 8, the optimal solution of equation parameters is realized by utilizing the daily basic flow and the genetic algorithm obtained by the water-withdrawal analysis and screening, the water-withdrawal constant and the BFImax optimal value are calculated day by day,
in the formula, a (t) is an optimization equation of the daily basic flow recession constant; fa (t) is a monthly recession constant Fourier fitting function; r (t) is a correction function; E. p is evaporation and rainfall respectively; t is time, typically in steps of 1 day; dtime is the fractional time after correction: (dtime＝decimal time-center of decimal time)；β 0 ～β 3 For the fitting parameters, the optimization objective function is as follows:
in the formula, Q i Day i base flow; the superscripts obs, sim and mean represent measured values, simulated values and mean values, respectively, wherein the base flow simulated value is obtained by dividing the ERDF, and the base flow measured value is obtained by screening from a daily measured runoff sequence according to the analysis of the backwater.
Preferably, in step 4, in order to increase the accuracy of the model, the model is more suitable for each land use type in the research area, X i Is fitted by genetic algorithms under conditions where the range interval is determined by looking up literature.
Preferably, in the step 5, a genetic algorithm based on MATLAB software is used for carrying out parameter optimization solution of the identification equation.
Preferably, in the step 7, according to the 2017 latest edition of classification of the current state of land utilization (GBT 21010-2017), the ARCGIS software and the remote sensing images are used for completing classification statistics of various land utilizations of the given watershed.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
according to the invention, the output coefficient model is further improved, so that the classification and partition quantitative source analysis of non-point source pollution in a research area more scientifically, reasonably and accurately can be realized through model simulation, the source monitoring and responsibility confirmation can be better carried out, and the reduction can be effectively carried out.
Drawings
FIG. 1 is a schematic representation of step I in example 3 of the present invention;
FIG. 2 is a schematic view of step II in example 3 of the present invention;
FIG. 3 is a schematic representation of step III of example 3 of the present invention;
FIG. 4 is a schematic view of step III in example 3 of the present invention;
FIG. 5 is a schematic view of step IV in example 3 of the present invention;
FIG. 6 is a verification diagram of the segmentation result of the base stream in example 4 of the present invention;
FIG. 7 is a graph showing verification of the base current TN loading capacity in example 4 of the present invention;
FIG. 8 is a graph showing the total runoff and the TN load capacity of the riverway daily in 11 months-2021 months in 2020 in the Shanghai river basin in example 4 of the present invention;
FIG. 9 is a graph of the non-point source pollution output coefficient of the upper mountain stream surface runoff TN in each land utilization type of the surface runoff in example 4 of the present invention;
FIG. 10 is a graph showing the non-point source nitrogen pollution in Yangxi river basin from No. 11/2020 to No. 10/2021 in example 4 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1:
a classification and partition identification method for non-point source pollution of watershed scale water comprises the following steps:
step 1: obtaining basic data and quantifying non-point source pollution load; the basic data acquisition comprises hydrological (flow) and water quality (pollutant concentration) data at an outlet of a given basin, meteorological data of the whole basin and land utilization data, wherein the hydrological and meteorological data day by day are provided by government related departments; the water quality data is obtained by regular (such as month step length) monitoring, and the analysis and determination method of the specific index refers to the national relevant standard. According to the 'current land utilization state classification' of the latest version of 2017 (GBT 21010-2017), the ARCGIS software and the remote sensing images are used for completing classification statistics of various land utilizations of the given drainage basin;
the non-point source pollution load quantification is based on discrete hydrological (flow) and water quality (pollutant concentration) data obtained by monitoring, and the daily runoff load at the outlet of the watershed is calculated by utilizing a LOADEST model developed by the American geological survey bureau:
in the formula (I), the compound is shown in the specification,
the method is characterized in that the total runoff load (kg. D) of the instantaneous river channel calculated by a corrected maximum likelihood estimation method is used -1 )；a 0 ～a j Parameters of a pollutant flux regression equation; h (a, b, s) 2 α, k) is an infinite number of likelihood approximation functions; a and b are functions that interpret variables; alpha and kappa are functions of gamma distribution; m is a degree of freedom; s 2 Is the residual error.
And 2, step: dividing and quantifying the basic flow and the load thereof;
the basic flow segmentation of the current domain is quantitatively determined by using two-parameter recursive filtering (ERDF) proposed by Eckhardt in 2005:
in the formula, q t 、Q t Respectively the total runoff and the base runoff at the time t; Δ t is the time step (days); alpha is a water-discharge constant (0) reflecting the water-discharge rate of the river<α<1)；BFI max Is the maximum base flow index. Due to BFI max It cannot be determined directly, eckhardt (2005) gives three empirical values: 0.80, 0.50 and 0.25, respectively corresponding to perennial rivers in porous medium aquifer areas and seasonal rivers and perennial rivers in hard rock medium aquifer areas.
The basic flow load segmentation method is called as recursive filtering basic flow load segmentation algorithm (RFLSA) for short, wherein the basic flow load segmentation quantification is based on hydrological and meteorological factors closely related to the basic flow load regression process, a regression statistical model of the basic flow load regression coefficient is constructed, see formula 3 for estimating the basic flow load regression coefficient day by day, after the load regression coefficient is determined, the regression statistical model is back-substituted into a recursive digital filtering equation, see formula 2, the daily load quantity obtained by LOADEST model simulation can be segmented to obtain the basic flow load quantity,
τ(t)＝γ 1 ×a(t)+γ 2 ×P(t)+γ 3 ×E(t)+C (3)
wherein tau is a base flow load shedding parameter; a is a water withdrawal parameter; p and E are rainfall and evaporation respectively; t is time (in days); gamma ray 1 ～γ 3 Is a fitting parameter;
and step 3: obtaining load data of a required area, land utilization types, unit areas of certain land utilization types (paddy fields, forest lands and the like), regional meteorological data, pollutant data of the atmosphere which is settled into a river water body, and pollutants generated by point source pollution;
and 4, step 4: constructing a non-point source pollution source analysis model:
in the formula, L is the river entering amount of non-point source pollutants; BL is base flow non-point source pollution load; x i The discharge amount of certain pollutants per unit area for a certain period (such as 1 day and 1 week) of the ith land utilization type, S i Is X i The correction parameter (c) can be expressed as a function of the meteorological factor; w (t) is the ratio of the surface runoff in a certain period of time to the average surface runoff in a research period of time; m (t) is a pollutant which is settled in the air and enters the river water body; d (t) is a pollutant generated by point source pollution; n is the number of land utilization types; t is time, generally days or weeks, P is rainfall data, E is evaporation data, T is average temperature data, R is relative humidity data, all are meteorological data, η represents the comprehensive influence of all factors except the factors contained in the function, and θ 1 ～θ 4 Solving the obtained equation fitting parameters for numerical optimization;
in order to increase the accuracy of the model and make it more suitable for each land use type in the research area, X i Under the condition that the range interval is determined by looking up the literatureFitting by a genetic algorithm;
and 5: data are obtained through the step 1, and parameter calibration of the non-point source pollution source analysis model is completed;
step 6: verifying and evaluating the source analysis model result: the data in step 1 is divided into two parts according to time sequence, the former part is used for determining and calibrating the model parameters in step 2, the latter part is used for verifying and evaluating the model simulation result, and the selected evaluation indexes are NSE, root mean square error-measured value standard deviation ratio (RSR) and decision coefficient (R) 2 ) The specific calculation method is as follows:
in the formula, L (i,m) And L (i,s) Respectively representing an actual measurement value and a simulation value of the load capacity of the ith polluted land; l is a radical of an alcohol avg An average value of measured values representing the pollutant load; n is the number of measured values;
the measured value can be obtained by calculating the instantaneous flow Q of the water flow and the corresponding continuous data of the pollutant concentration C under the flow condition through the L = KCQ formula, and the analog value is obtained by a classification partition identification formula;
in the formula of L = KCQ, L is load; k is a unit conversion coefficient; c concentration, Q is river flow, the formula is an estimation formula of water pollution load, the pollution load is the total amount of pollutants entering the water body from a point pollution source and a surface pollution source within a certain period of time;
and 7: classifying and identifying the non-point source pollution source, and performing statistical analysis on various data in the river basin by using the statistical analysis function of ARCGIS software to obtain various land utilization areas of the river basin in the research area;
according to the 'current land utilization state classification' of the latest version of 2017 (GBT 21010-2017), the ARCGIS software and the remote sensing image are utilized to complete classification statistics of various land utilizations of the given drainage basin. The land utilization types of the research area are divided into paddy fields, grasslands, forest lands, water areas, dry lands and human habitats. Substituting the obtained improved output coefficient model into the meteorological data in the land areas and the required time periods of all the land types to respectively obtain the surface runoff non-point source pollution discharge amount of all the land types in the research area within a certain time;
and 8: and identifying the non-point source pollution source partition, performing statistical analysis on various data in the flow domain by using the statistical analysis function of ARCGIS software, dividing the research area into required grades through national villages and towns partitions, and substituting the land area of each administrative area and the meteorological data in a required time period by using the obtained output coefficient model of the circumference scale to respectively obtain the surface runoff discharge amount of each administrative area in the research area within a certain time.
In the embodiment, non-point source pollution treatment can be performed in a targeted manner, more reasonably, scientifically and accurately through classification and partition identification.
Example 2
The implementation contents of the above embodiments can be referred to the above description, and the embodiments herein are not repeated in detail; in the embodiment of the present application, the difference from the above embodiment is:
in this embodiment, the method for determining the ERDF parameter includes the following steps:
s1, according to an empirical formula, N =0.83A 0.2 (N is the number of days required for the surface runoff to completely stop after the flood peak value; A is the area of the watershed in km 2 ) Determining a starting point of pure base flow water withdrawal;
s2, the condition y is satisfied 1 >y 2 >…>y k >y k+1 >y k+2 Flow data y of k And y k+1 Screening out the daily flow sequence to obtain a pure base flow water withdrawal process; selected process of water withdrawalThe minimum length of the time interval is 5 days, and in areas with high rainfall occurrence frequency, rainfall interference needs to be eliminated as much as possible during the analysis of water return;
s3, fitting a scatter diagram (y) by using a linear equation passing through the origin k vs y k+1 ) The slope of the equation obtained from the upper boundary point of (1) is the water-withdrawal constant of the ERDF;
s4, calculating the average water-removing constant and the monthly scale water-removing constant of the whole process according to the method;
s5, in order to reduce the variability of the base flow recession rule and the parameter BFI in the different base flow recession processes max Obtaining a first-order Fourier fitting function of a daily water withdrawal constant based on uncertainty of a basic flow segmentation result caused by empirical value taking; then, on the basis of the function, a calculation equation of the daily recession constant of the ERDF is constructed, see formula 8, the optimal solution of equation parameters is realized by utilizing the daily basis flow obtained by analyzing and screening the recession and the genetic algorithm, and the daily recession constant and BFI are obtained by calculation max The optimum value of the number of the optical fibers,
in the formula, a (t) is an optimization equation of the daily basic flow water withdrawal constant; fa (t) is a monthly recession constant Fourier fitting function; r (t) is a correction function; E. p is evaporation and rainfall respectively; t is time, typically in 1 day steps; dtime is the fractional time after correction (dtime = decimal time-center of decimal time); beta is a 0 ～β 3 For the fitting parameters, the optimization objective function is as follows:
in the formula, Q i Day i base flow; the superscripts obs, sim and mean represent measured values, simulated values and mean values, respectively, wherein the base flow simulated value is obtained by dividing the ERDF, and the base flow measured value is obtained by screening from a daily measured runoff sequence according to the analysis of the backwater.
Example 3
The implementation contents of the above embodiments can be referred to the above description, and the embodiments herein are not repeated in detail; in the embodiment of the present application, the difference from the above embodiment is:
referring to fig. 1-5, the process of matlab to identify equation parameter value optimization solution is as follows:
i: opening an MATLAB program, finding a column of an application program in a main tool column, and opening an Optimization option;
II: in the Optimization program, different models are selected according to different problem types, constraints are input at the same time, and the like;
III: the conditions under which the optimization program runs are selected. Adding conditions for optimizing program operation, such as optimization cutoff criteria and the like, in the middle column;
IV: and starting operation, clicking the Start to realize the operation of the optimization program, and displaying an operation result in a frame shown in the figure.
In this embodiment, the closer the nash coefficient is to 1, the more reliable the simulation result representing the model, and the corresponding improved output coefficient model is obtained by substituting each unknown value in the operation result frame corresponding to the maximum nash coefficient into the identification equation.
Example 4
The implementation contents of the above embodiments can be referred to the above description, and the embodiments herein are not repeated in detail; in the embodiment of the present application, the difference from the above embodiment is:
in the embodiment, on the basis of fully considering factors such as water system distribution of the upper stream area, river terrain, pollutant discharge conditions, vegetation and water and soil loss, water quality monitoring sites are arranged, and the hydrology and water quality conditions at the outlet of the upper stream area are regularly monitored in 11-2021-10 months in 2020.
In this embodiment, the water quality monitoring frequency is once per week; the hydrological flow monitoring adopts a flow rate meter method, takes day as a step length, and specifically refers to river flow test specification (GB 50179-2015). A water sample is collected from the middle of a river channel 30cm away from the water surface and is filled in a 2.5L polyethylene bottle. And (3) immediately transporting the water sample back to a laboratory after the water sample is collected, measuring total nitrogen by using an alkaline potassium persulfate digestion ultraviolet spectrophotometry within 24 hours after water collection, and calculating the non-point source nitrogen pollution Load capacity on the daily scale of the research area by using a Load Estimator (LOADEST) model on the basis of the discrete hydrological water quality data obtained by monitoring.
In the embodiment, meteorological data day by day at a meteorological station in Mingan county is obtained by Mingan county meteorological office 2020.11-2021.11 Mingan county. And (3) carrying out statistical analysis on various data in the flow domain by using the statistical analysis function of the ARCGIS software to obtain various land utilization areas of the upstream stream domain.
In this embodiment, according to the research report of the Minian county government, the influence of point source pollution of rural residents on the TN load capacity of the local watershed is not considered. By referring to relevant literature data and combining with the actual situation of the research area, the amount of atmospheric dry settlement of TN in the thousand island lake basin is 2.24kg (hm & a) -1 。
The results of the simulation of calling the model auto-selection term of the load test model to obtain the candidate model are shown in tables 1 and 2.
TABLE 1 Sun-by-day TN load quantitative regression equation of Chinese scholar stream domain
In this embodiment, note: in the formula, L is daily load capacity (t.d) of pollutants -1 ) (ii) a Q is the runoff (mm); dtime is the score date after centering (dtime = score date-center score date); a is a 0 、a 1 、a 2 、a 3 、a 4 、a 5 And a 6 Is the equation coefficient; the SCR is a residual sequence correlation coefficient and is used for verifying the result of a source analysis model and evaluating whether sequence correlation exists in the residual, the smaller the value of the residual is, the mutual independence between the residual is shown, and the sequence correlation does not exist in the related variables in the equation; PPCC is probability curve correlation coefficient, and source analysis model result verification and evaluationWhether the residuals of the model follow a standard normal distribution.
LOADEST model parameter calibration and evaluation of day-by-day TN load estimation in the Chinese parasol stream region in Table 2
In the embodiment, the single annual water withdrawal constant is replaced by the daily water withdrawal constant obtained by back-stepping based on the fitted fourier function and the fraction date, so that the basic flow segmentation result has higher accuracy and better simulation precision (NSE =0.81, rsr =0.44, r) 2 =0.94, fig. 6).
In this embodiment, the RFLSA-based base stream TN load quantification result can better reflect the variation trend (R) of the base stream TN actual measurement load on the daily scale of the current domain 2 = 0.91) while also having a high analog accuracy (NSE =0.89, rsr =0.33, fig. 7). The total runoff, the basal runoff and the corresponding TN load change of the riverway day by day in the Shanghai river basin from 11 months to 10 months in 2021 in 2020 are shown in FIG. 8.
In this embodiment, the surface runoff TN discharge amount of the shanghai river area 2020.11-2021.10 is divided into single-week and double-week periods, the single-week data is used for the calibration of the model parameters, and the double-week data is used for the verification and evaluation of the model.
In this embodiment, a genetic algorithm of MATLAB software is used to perform correlation analysis on surface runoff TN discharge and meteorological factors such as rainfall (P), evaporation (E), average temperature (T), relative humidity (R) of the Shanghai river basin in each week from 2020 to 2021 in each month of 10, and the correlation analysis is performed to obtain information about S i (t) fitting equation:
S i (t)＝0.000097×P(t)-0.000223×E(t)-0.000287×T(t)+0.000145×R(t)+0.001818
in the embodiment, the value range of the output coefficient of each land use type is roughly determined by referring to the reference document of the Jiangzhe area as the research area and combining the actual situation of the research area, so that the value of the output coefficient has relative accuracy. Using the improved output coefficient model, performing parameter fitting by using an MATLAB genetic algorithm to obtain each land use output coefficient value with a cycle as a step length, as shown in table 3:
TABLE 3 week output coefficient of land use types of Wuxi surface runoff TN
In this embodiment, the change trend (R) of the surface runoff TN actual measurement load on the week (time) scale of the upper stream area can be better reflected by the surface runoff TN load calculated by the identification equation obtained by parameter optimization through the genetic algorithm of the MATLAB software through the substitution of the biweekly data for verification 2 = 0.80) while also having a high analog accuracy (NSE =0.80, rsr = 0.44).
In the embodiment, the non-point source pollution TN emission of each land utilization type in the research area is calculated by using the improved output coefficient model. In 11-2021 in 2020, the total nitrogen load of the surface runoff of the upland stream area is 56.06 tons, as shown in fig. 10, wherein the non-point source nitrogen pollution load of paddy field, grassland, woodland, dry land, water area, human habitat and atmospheric settlement is 5761.23kg, 5507.77kg, 28668.62kg, 750.32kg, 5.83kg, 26.79kg and 15335.04kg, and the contribution rates are 10.28%, 9.83%, 51.14%, 1.34%, 0.01%, 0.05% and 27.36%, respectively. In the embodiment, the TN load contribution rate of the surface runoff of the forest land (containing economic forest) is the highest because the proportion of the forest land in the total area of the river basin is as high as 79.57 percent
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A classification and partition identification method for non-point source pollution of a watershed scale water body is characterized by comprising the following steps:
step 1: obtaining basic data and quantifying non-point source pollution load; wherein the basic data acquisition comprises hydrological (flow) and water quality (pollutant concentration) data at the outlet of a given basin, meteorological data and land utilization data of the whole basin; the non-point source pollution load quantification is based on discrete hydrological (flow) and water quality (pollutant concentration) data obtained by monitoring, and the daily runoff load at the outlet of the drainage basin is calculated by utilizing a LOADEST model developed by the American geological survey bureau:
in the formula (I), the compound is shown in the specification,
the method is characterized in that the instantaneous river total runoff load (kg. D) calculated by using a modified maximum likelihood estimation method -1 )；a 0 ～a j Parameters of a pollutant flux regression equation; h (a, b, s) 2 α, k) is a likelihood approximation function of infinite series; a and b are functions that interpret variables; alpha and kappa are functions of gamma distribution; m is a degree of freedom; s 2 Is a residual error;
and 2, step: dividing and quantifying the basic flow and the load thereof;
the basic flow segmentation quantification of the current domain adopts two-parameter recursive filtering (ERDF) proposed by Eckhardt in 2005:
in the formula, q t 、Q t Respectively the total runoff and the base runoff at the time t; Δ t is the time step (days); alpha is a water-discharge constant (0) reflecting the water-discharge rate of the river<α<1)；BFI max At maximum base flow index due to BFI max It cannot be determined directly, and Eckhardt (2005) gives three empirical values: 0.80, 0.50 and 0.25 corresponding to perennial rivers and seasonal rivers and hard rock media in porous medium aquifer regions, respectivelyPerennial rivers in aquifer areas;
on the basis of realizing the basic flow segmentation quantification, a regression statistical model of basic flow load regression coefficients is constructed on the basis of hydrological and meteorological factors closely related to the basic flow load regression process, see formula 3, for estimating the basic flow load regression coefficients day by day, after the load regression coefficients are determined, the regression statistical model is back-substituted into a recursive digital filtering equation, see formula 2, so that the daily load quantity obtained by the LOADEST model simulation can be segmented to obtain the basic flow load quantity, the basic flow load quantification segmentation method is called recursive filtering basic flow load segmentation algorithm (RFLSA) for short,
τ(t)＝γ 1 ×a(t)+γ 2 ×P(t)+γ 3 ×E(t)+C (3)
wherein tau is a base stream load shedding parameter; a is a water withdrawal parameter; p and E are rainfall and evaporation respectively; t is time (in days); gamma ray 1 ～γ 3 Is a fitting parameter;
and step 3: obtaining load data of a required area, land utilization types, unit areas of certain land utilization types (paddy fields, forest lands and the like), regional meteorological data, pollutant data of the atmosphere which is settled into a river water body, and pollutants generated by point source pollution;
and 4, step 4: constructing a non-point source pollution source analysis model:
in the formula, L is the river entering amount of the non-point source pollutant; BL is the base flow non-point source pollution load; x i The discharge amount of certain pollutants per unit area for a certain period (such as 1 day and 1 week) of the ith land utilization type, S i Is X i The correction parameter (c) can be expressed as a function of the meteorological factor; w (t) is the ratio of the surface runoff in a certain period of time to the average surface runoff in a research period of time; m (t) is a pollutant which is settled in the air and enters the river water body; d (t) is a pollutant generated by point source pollution; n is the number of land utilization types; t is time, generally days or weeks, P is rainfall data, EEvaporation data, T mean temperature data, R relative humidity data, are meteorological data, η represents the combined effect of all factors except the factor contained in the function, θ 1 ～θ 4 Solving the obtained equation fitting parameters for numerical optimization;
and 5: data are obtained through the step 1, and parameter calibration of the non-point source pollution source analysis model is completed;
step 6: calibration and verification: dividing the data in the step 1 into two parts according to the time sequence, wherein the former part is used for determining and calibrating the model parameters in the step 2, the latter part is used for verifying and evaluating the simulation result of the model, and the selected evaluation indexes are NSE, root mean square error-measured value standard deviation ratio (RSR) and decision coefficient (R) 2 ) The specific calculation method is as follows:
in the formula, L (i,m) And L (i,s) Respectively representing an actual measurement value and an analog value of the load capacity of the ith polluted land; l is a radical of an alcohol avg An average value of measured values representing the pollutant load; n is the number of measured values;
the measured value can be obtained by calculating based on the instantaneous flow Q of the water flow and corresponding pollutant concentration C continuous data under the flow condition through the L = KCQ formula, and the analog value is obtained by a classification and partition identification formula;
in the formula of L = KCQ, L is load; k is a unit conversion coefficient; c concentration, Q is river flow, the formula is an estimation formula of water pollution load, the pollution load is the total amount of pollutants entering the water body from a point pollution source and a surface pollution source in a certain period of time;
and 7: classifying and identifying non-point source pollution sources, and completing statistics and summary of various land utilization areas in the region by using ARCGIS software and corresponding land utilization maps; combining the output coefficient model of the output cycle scale which has completed parameter optimization and verification to calculate and obtain the non-point source pollution discharge amount of the surface runoff in the given time period of different land types of the drainage basin;
and step 8: and identifying non-point source pollution source partitions, performing statistical analysis on various data in the flow field by using the statistical analysis function of ARCGIS software, dividing the research area into required grades through national villages and towns partitions, and substituting the land areas of each administrative area and the meteorological data in a required time period by using the obtained week scale output coefficient model to respectively obtain the non-point source pollution discharge amount of the surface runoff in a given time period of each administrative area block in the research area.
2. The method for identifying non-point source pollution classification partitions of watershed-scale water bodies according to claim 1, wherein the hydrological and meteorological data day by day in the step 1 are provided by government-related departments; the water quality data is obtained by monitoring regularly (such as with a month as a step length), the analysis and determination method of the specific indexes refers to the related national standards, and according to the 2017 latest edition 'classification of the current land utilization' (GBT 21010-2017), the classification and statistics of various land utilizations of the given watershed are completed by using ARCGIS software and remote sensing images.
3. The method for classifying and distinguishing non-point source pollution of watershed scale water body according to claim 1, wherein BFI is used in the step 2 max Cannot be obtained by direct measurement at present, BFI max The value of (A) is obtained by calculating and simulating by using a mathematical algorithm on the basis of combining with the hydrogeological characteristics of the drainage basin: BFI of perennial rivers and seasonal rivers in porous medium aquifer regions and perennial rivers in hard rock medium aquifer regions max Suggested values are 0.80, 0.50 and 0.25, respectively.
4. The method for classifying and identifying the non-point source pollution of the watershed scale water body according to claim 3, wherein the method for determining the ERDF parameter comprises the following steps:
s1, according to an empirical formula, N =0.83A 0.2 (N is the number of days required for the surface runoff to completely stop after the flood peak; A is the drainage basin area in km 2 ) Determining a starting point of pure base flow water withdrawal;
s2, the condition y is satisfied 1 >y 2 >…>y k >y k+1 >y k+2 Flow data y of k And y k+1 Screening out the daily flow sequence to obtain a pure base flow water withdrawal process; the minimum length of the selected water return process is 5 days, and in areas with high rainfall occurrence frequency, the interference of rainfall needs to be eliminated as much as possible during water return analysis;
s3, fitting a scatter diagram (y) by using a linear equation passing through the origin k vs y k+1 ) The slope of the equation obtained from the upper boundary point of (2) is the water withdrawal constant of the ERDF;
s4, calculating an average water-withdrawing constant and a monthly scale water-withdrawing constant in the whole process according to the method;
s5, in order to reduce the variability of the base flow recession rule and the parameter BFI in the different base flow recession processes max Obtaining a first-order Fourier fitting function of a daily recession constant on the basis of uncertainty of a basic flow segmentation result caused by empirical value taking; then, on the basis of the function, a calculation equation of the daily recession constant of the ERDF is constructed, see formula 8, the optimal solution of equation parameters is realized by utilizing the daily basis flow obtained by analyzing and screening the recession and the genetic algorithm, and the daily recession constant and BFI are obtained by calculation max The optimum value of the first and second,
in the formula, a (t) is an optimization equation of the daily basic flow recession constant; fa (t) is a monthly recession constant Fourier fitting function; r (t) is a correction function; E. p is evaporation and rainfall respectively; t is time, 1Step size is often 1 day; dtime is the fractional time after correction (dtime = decimal time-center of decimal time); beta is a 0 ～β 3 For the fitting parameters, the optimization objective function is as follows:
in the formula, Q i Day i base flow; the superscripts obs, sim and mean represent measured values, simulated values and mean values, respectively, wherein the base flow simulated value is obtained by dividing the ERDF, and the base flow measured value is obtained by screening from a daily measured runoff sequence according to the analysis of the backwater.
5. The method for classifying and identifying non-point source pollution of watershed scale water body according to claim 1, wherein in the step 4, in order to increase the model accuracy and make the model more suitable for various land utilization types in the research area, X i Fitting by genetic algorithm under the condition of determining the range interval by looking up the literature.
6. The method for identifying the non-point source pollution classification partitions of the watershed-scale water body according to claim 1, wherein a genetic algorithm based on MATLAB software is used for carrying out parameter optimization solution on an identification equation in the step 5.
7. The method for identifying the classification and the partition of the non-point source pollution of the watershed scale water body as claimed in claim 1, wherein in the step 7, according to the 2017 latest edition of classification of the current state of land utilization (GBT 21010-2017), the ARCGIS software and the remote sensing image are used for completing the classification and the statistics of various land utilizations of the given watershed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210527218.7A CN115188434A (en) | 2022-05-16 | 2022-05-16 | Watershed-scale water body non-point source pollution classification and partition identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210527218.7A CN115188434A (en) | 2022-05-16 | 2022-05-16 | Watershed-scale water body non-point source pollution classification and partition identification method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115188434A true CN115188434A (en) | 2022-10-14 |
Family
ID=83513120
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210527218.7A Pending CN115188434A (en) | 2022-05-16 | 2022-05-16 | Watershed-scale water body non-point source pollution classification and partition identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115188434A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115712813A (en) * | 2022-11-23 | 2023-02-24 | 北京中科三清环境技术有限公司 | Method and device for determining pollution load output coefficient of non-point source and electronic equipment |
| CN116564431A (en) * | 2023-06-02 | 2023-08-08 | 江苏捷利达环保科技有限公司 | Pollution source online analysis system and method based on big data processing |
-
2022
- 2022-05-16 CN CN202210527218.7A patent/CN115188434A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115712813A (en) * | 2022-11-23 | 2023-02-24 | 北京中科三清环境技术有限公司 | Method and device for determining pollution load output coefficient of non-point source and electronic equipment |
| CN116564431A (en) * | 2023-06-02 | 2023-08-08 | 江苏捷利达环保科技有限公司 | Pollution source online analysis system and method based on big data processing |
| CN116564431B (en) * | 2023-06-02 | 2024-01-09 | 江苏捷利达环保科技有限公司 | Pollution source online analysis system and method based on big data processing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022184088A1 (en) | Flood sensitivity risk assessment method based on ensemble learning | |
| CN115188434A (en) | Watershed-scale water body non-point source pollution classification and partition identification method | |
| Zhang et al. | An evaluation of the ecological and environmental security on China’s terrestrial ecosystems | |
| US20230054713A1 (en) | Method for determining contribution rate of pollution load in water quality assessment section of annular river network system based on water quantity constitute | |
| CN112001610A (en) | Method and device for treating agricultural non-point source pollution | |
| CN109933637B (en) | Flood risk dynamic display and analysis system | |
| CN114723179A (en) | Water environment early warning traceability system and method based on water yield and water quality combined control | |
| CN114168906A (en) | Mapping geographic information data acquisition system based on cloud computing | |
| CN113095694B (en) | Rainfall sand transportation model construction method suitable for multiple landform type areas | |
| CN111090831A (en) | Lake area change key driving factor identification method | |
| CN113011993A (en) | Method for measuring and calculating water-entering load of agricultural pollution source based on standard data | |
| CN116644889A (en) | Water pollution tracing method and terminal | |
| CN115409374A (en) | Method for early warning influence of urbanization difference on watershed water ecological service function | |
| CN110378611A (en) | Sponge control index based on GIS examines and appraisal procedure and system | |
| Chen et al. | Trends in anthropogenic ammonia emissions in China since 1980: A review of approaches and estimations | |
| CN116050703B (en) | Village construction and resource environment bearing capacity coordination evaluation and coordination mode analysis method | |
| Pomari et al. | A new tool to assess ecosystem health in large subtropical reservoirs: Development and validation of a Planktonic Index of Biotic Integrity | |
| CN112287299A (en) | River health change quantitative attribution method, device and system | |
| CN115936545A (en) | Two-parameter monthly scale hydrological model building method based on hydrothermal complementary equation | |
| CN112001562A (en) | Irrigation water data verification method and device | |
| CN111310124A (en) | Urban runoff data processing method | |
| Wu et al. | Impact of surface and underground water uses on streamflow in the upper-middle of the Weihe River basin using a modified WetSpa model | |
| CN115453664A (en) | Rainfall runoff forecasting method suitable for data-free areas | |
| CN115186960A (en) | Accounting method and device for effective collection and treatment capacity of urban sewage | |
| CN114595631A (en) | Water quality prediction method based on EFDC model and machine learning algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |