CN109784568A - A method of lake water quality model prediction is carried out by multiple target analysis of uncertainty - Google Patents
A method of lake water quality model prediction is carried out by multiple target analysis of uncertainty Download PDFInfo
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Abstract
The invention discloses a kind of methods for carrying out lake water quality model prediction by multiple target analysis of uncertainty, comprising the following steps: (1) determines the model and its parameter for carrying out water quality prediction；(2) value range and distribution characteristics of model parameter are determined；(3) the multiple objective functions for carrying out parameter uncertainty analysis are determined；(4) value of objective function in corresponding situation is determined；(5) compare the corresponding parameter set that is dominant of Pareto set obtained under objective function；(6) carry out two-parameter analysis of uncertainty: (7) are using kernel function estimation and draw each parameter in the Density Distribution of concentration that is dominant, and the corresponding value of probability density highest point is parameter rating of the model result；(8) parameter for collecting boundary condition needed for predicting and calibration substitutes into model together, carries out water quality prediction.The present invention can give simultaneously the uncertainty and parameter calibration of water quality model parameter, and be predicted, the time required for model use can greatly be saved, and provide reference information and foundation for lake water quality management.
Description
Technical field
The present invention relates to a kind of methods for carrying out lake water quality model prediction by multiple target analysis of uncertainty, belong to lake
Moor water quality management field.
Background technique
It is influenced by industrial or agricultural disposal of pollutants, China's major part lake water quality situation aggravates, and pollution problem is frequent.For
Protection lake water quality and ecological safety, carrying out pollution prediction with lake water quality model is a kind of necessary means.It is using
During lake water quality model is predicted, analysis of uncertainty is carried out to the parameter of model and calibration is extremely important step
Suddenly.Currently, the analysis of uncertainty of water quality model is mostly but target analysis of uncertainty is not enough to reflect lake water quality and life
The diversity of state；And existing multiple target analysis of uncertainty is analyzed due to needing to be translated into single goal by weight, because
The selection of this weight is very subjective random.In addition, the calibration of water quality model parameter and parameter uncertainty analysis mutually divide at present
From, also needed after completing analysis of uncertainty to parameter calibration optimize, this greatly increase model application be calculated as
This, greatly hinders the promotion and application of model prediction.
Summary of the invention
It is an object of the invention in view of the above problems, in order to overcome the deficiencies in the prior art, provide
A method of lake water quality model prediction is carried out by multiple target analysis of uncertainty.This method can be objectively to water quality mould
The parameter of type carries out multiple target analysis of uncertainty, and is completed at the same time the calibration work of parameter, to greatly reduce Lake Water
The amount of calculation of matter model prediction.
The object of the present invention is achieved like this, and one kind is pre- by multiple target analysis of uncertainty progress lake water quality model
The method of survey, which comprises the following steps:
(1) lake for the water quality predicted and its monitoring data of boundary river are collected, determine the mould for carrying out water quality prediction
Type and its parameter；
(2) value range and distribution characteristics of model parameter are determined, and according to respective distribution characteristics in value range
M group parameter value is extracted, m is greater than or equal to 10000；
(3) determine that n objective function for carrying out model parameter uncertainty analysis, n are greater than or equal to 2；
(4) monitoring data of the value of m group model parameter, the monitoring data of boundary river and lake itself are substituted into water quality
Model is calculated, and determines the m group value of objective function；
(5) compare the corresponding parameter set that is dominant of Pareto set obtained under objective function, parameter is dominant the parameter group of concentration
Number d is not less than 500；Increase the parameter group number m extracted in step (2) if d is less than 500, and repeats step (2)-
(4)；
(6) the two-parameter joint probability density function for drawing the parameter set that is dominant, carries out two-parameter analysis of uncertainty；
(7) using kernel function estimation and each parameter is drawn in the probability density distribution of concentration that is dominant, probability density is most
The corresponding value of eminence is as parameter rating of the model result；
(8) monitoring data of boundary river needed for predicting are collected and the model parameter of calibration substitutes into water quality mould together
Type carries out water quality prediction.
In the step (1):
A. the lake for the water quality predicted collected and its monitoring data of boundary river are 2 months at least continuous, at least flat
1 times a week, monitoring data include the water monitoring data in predicted lake, lake boundary river water monitoring data and
The data on flows of boundary river；
B. referred to according to the water quality for including in the lake for the water quality predicted and its monitoring data of boundary river collected
Mark and the index predicted determine the model for predicting lake water quality, and the ginseng of the model is determined according to its model structure
Number.
The step (2) specifically includes the following steps:
A. the value range and its distribution of model parameter are determined, it will be assumed that model parameter is in its respective value range
It is uniformly distributed；
B. in the respective value range of each parameter according to its distribution form, with random number generator generate m with
Machine number, m need to be more than or equal to 10000；
C. the stochastic parameter value of generation is grouped according to model needs, it is ensured that each parameter has and only has in every group
1 value, forming m group altogether can be used for the parameter group of driving model.
The step (3) specifically includes the following steps:
A. water quality prediction is required to analyze；
B. according to analysis requirement result, determine that n objective function for carrying out parameter uncertainty analysis, n are greater than or equal to
2；Objective function can for varying environment index pattern die analog values S and monitor value O average relative error RE and they
Root-mean-square error RMSE is specifically shown in following two formula:
Wherein OiIt is i-th of monitor value, SiIt is and OiCorresponding pattern die analog values, T are monitor value sums.
The step (4) specifically includes the following steps:
A. the value of m group model parameter, the monitoring data of corresponding boundary river are substituted into water quality model and calculated, really
Determine the analogue value S of each environmental index of m groupi, i=1,2,3...T；
B. the monitoring data O in lake itself is combinedi, i=1,2,3...T, calculate the value of every group of objective function, total m group.
The step (5) specifically includes the following steps:
A. distinguish objective function property, be classified as that the bigger representative simulation result of objective function is better and objective function more
The small better two major classes of representative simulation result；Negative sign will be added before a kind of objective function that objective function is the bigger the better, makes two classifications
It is better that scalar functions are unified for the smaller representative simulation result of objective function；
If b. defining the corresponding n objective function of parameter group u is both greater than the corresponding n objective function of parameter group v；Then think
Parameter group u is dominant by parameter group v；
C. compare the corresponding objective function of m group parameter, will wherein it is all under above-mentioned steps b definition by certain any group parameter
The parameter group removal being dominant；The collection of remaining d group target function value is collectively referred to as Pareto set, and d is not less than 500；
D. the corresponding parameter group of Pareto set is extracted, d group parameter is obtained and forms the parameter set that is dominant；
E. increase the parameter group number m extracted in step (2) if d is less than 500, and repeat step (2)-(4).
The step (6) specifically includes the following steps:
A. the two-parameter joint probability density function for the parameter set that is dominant is drawn；
B. carry out two-parameter analysis of uncertainty with ocular estimate: the big local parameter uncertainty of probability density is small, probability
The small local parameter uncertainty of density is big.
The step (7) specifically includes the following steps:
A. using kernel function estimation and each parameter is drawn in the probability density distribution of concentration that is dominant；Its Kernel Function is adopted
Use gaussian kernel function；
B. the corresponding value of select probability density highest point is as parameter rating of the model result；
The step (8) specifically includes the following steps:
A. the model parameter that calibration is completed is arranged；
B. the monitoring data of boundary river needed for predicting are collected and the model parameter of calibration substitutes into water quality model together,
Carry out water quality prediction.
In the present invention, a method of lake water quality model prediction is carried out by multiple target analysis of uncertainty, passes through fortune
The multiple target that lake water quality model parameter can objectively be provided with the concept of Pareto set is uncertain, and uncertain in analysis
The result of parameter rating of the model is provided while property.The following steps are included: (1) collects the lake and its boundary for the water quality predicted
The monitoring data in river determine the model and its major parameter for carrying out water quality prediction；(2) empirically determined major parameter takes
It is worth range and distribution characteristics, and extracts multiple groups parameter value according to respective distribution characteristics in value range；(3) it determines and carries out
Multiple objective functions of parameter uncertainty analysis；(4) by the monitoring number of the value of each group parameter, boundary condition and water body itself
It is calculated according to Eutrophication Model is substituted into, determines the value of objective function in corresponding situation；(5) compare and obtain under objective function
The corresponding parameter set that is dominant of Pareto set；(6) the two-parameter joint probability density function for the parameter set that is dominant is drawn, is carried out double
Parameter uncertainty analysis: (7) are using kernel function estimation and draw each parameter in the Density Distribution of concentration that is dominant, and probability is close
Spending the corresponding value of highest point is parameter rating of the model result；(8) parameter one of boundary condition and calibration needed for predicting is collected
It rises and substitutes into model, carry out water quality prediction.The present invention can give the uncertainty and parameter calibration of water quality model parameter simultaneously, and
It is predicted, the time required for model use can greatly be saved, provide reference information and foundation for lake water quality management.
The utility model has the advantages that a kind of method for carrying out lake water quality model prediction by multiple target analysis of uncertainty, Neng Gouke
The multiple target analysis of uncertainty for providing lake water quality model parameter seen as a result, and provide the knot of parameter rating of the model at the same time
Fruit greatly reduces subjectivity and arbitrariness in lake water quality model parameter multiple target analysis of uncertainty, and reduces utilization
Model huge calculation amount required for being predicted.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
The two-parameter joint probability density and respective kernel function estimation probability density figure that Fig. 2 is KTB and BMR.
The two-parameter joint probability density and respective kernel function estimation probability density figure that Fig. 3 is PM and KESS.
Fig. 4 is model prediction result.
Specific embodiment
The present invention will be further explained with the actual conditions in east China lake with reference to the accompanying drawing.
(1) according to flow chart shown in Fig. 1, step (1) includes:
A. the lake year website first arrival in July has been collected by the end of September, average cyanobacteria 1 times a week, ammonia nitrogenNitrate nitrogenAnd phosphoric acidMonitor value, totally 11 groups of monitor values.The lake is obtained simultaneously
The flow within the period in main 8 rivers of Bo Huanhu and their cyanobacteria, ammonia nitrogenNitrate nitrogenAnd phosphoric acidEtc. main environments index monitoring data, these data will as boundary condition participate in model
It calculates；
B. according to the water quality indicator and major prognostic blue alga biomass this case collected, use environment hydrodynamic force is determined
Code (Environmental Fluid dynamic Code, EFDC) this model is predicted.The parameter of the model is 4
A, concrete meaning see the table below:
(2) according to shown in flow chart, step (2) includes:
A. the value range according to determining major parameter is as shown in the table, it is assumed that they uniformly divide in respective range
Cloth；
B. according to its distribution form in the respective value range of each parameter, 11000 are generated with random number generator
A random number；
C. the stochastic parameter value of generation is grouped according to model needs, it is ensured that each parameter has and only has in every group
1 value, forms 11000 groups of parameters altogether.
(3) according to shown in flow chart, step (3) includes:
A. require to analyze to water quality prediction: the main target of this prediction is blue alga biomass；
B. according to analysis requirement result, determine that the objective function for carrying out parameter uncertainty analysis is cyanobacteria, ammonia nitrogenNitrate nitrogenAnd phosphoric acidThe relative error RE of analog result, is calculated using following formula:
Wherein OiIt is i-th of monitor value, SiIt is and OiCorresponding pattern die analog values, T are monitor value sums, are equal to herein
11。
(4) according to shown in flow chart, step (4) includes:
A. the value of 11000 groups of parameters, corresponding boundary condition are substituted into Eutrophication Model and calculated, determined
11000 groups of cyanobacterias, ammonia nitrogensNitrate nitrogenAnd phosphoric acidCalculated value Si, i=1,2,
3...T；
B. the monitoring data O of water body itself is combinedi, i=1,2,3...T, calculate the value of 11000 groups of relative errors.
(5) according to shown in flow chart, step (5) includes:
A. distinguish objective function property, be classified as that the bigger representative simulation result of objective function is better and objective function more
The small better two major classes of representative simulation result；Negative sign will be added before a kind of objective function that objective function is the bigger the better, makes two classifications
It is better that scalar functions are unified for the smaller representative simulation result of objective function；
In this calculating, the smaller representative simulation result of relative error is better, therefore does not need to be converted.
If b. defining corresponding 4 relative errors of parameter group u is both greater than corresponding 4 relative errors of parameter group v；Then think
Parameter group u is dominant by parameter group v；
C. compare the corresponding objective function of 11000 groups of parameters, will wherein it is all above-mentioned steps b definition under certain group parameter groups
The parameter group removal being dominant；The collection of remaining more than 600 group relative error is collectively referred to as Pareto set；
D. the corresponding parameter group of Pareto set is extracted, more than 600 group parameters is obtained and forms the parameter set that is dominant.
(6) according to shown in flow chart, step (6) includes:
A. the two-parameter joint probability density function for the parameter set that is dominant is drawn；As a result see Fig. 2 and Fig. 3；
B. carry out two-parameter analysis of uncertainty with ocular estimate: the big local parameter uncertainty of probability density is small, probability
The small local parameter uncertainty of density is big.It can be seen from the figure that 4 parameters are being worth relatively large local probability density
Greatly, i.e., herein uncertainty is small；And uncertainty is smaller at remaining distribution.
(7) according to shown in flow chart, step (7) includes:
A. using kernel function estimation and each parameter is drawn in the probability density distribution of concentration that is dominant；Its Kernel Function is adopted
Use gaussian kernel function；As a result see Fig. 2 and Fig. 3；
B. the corresponding value of select probability density highest point is as parameter rating of the model as a result, as a result see the table below:
(8) according to shown in flow chart, step (8) includes:
A. the model parameter that calibration is completed is arranged；
B. it collects boundary condition needed for predicting and the parameter of calibration substitutes into model together, carry out water quality prediction.Cyanobacteria
Biomass prediction result is shown in Fig. 4.
Claims (9)
1. it is a kind of by multiple target analysis of uncertainty carry out lake water quality model prediction method, which is characterized in that including with
Lower step:
(1) collect the water quality predicted lake and its boundary river monitoring data, determine progress water quality prediction model and
Its model parameter；
(2) value range and distribution characteristics of model parameter are determined, and extracts m according to respective distribution characteristics in value range
Group parameter value, m are greater than or equal to 10000；
(3) determine that n objective function for carrying out model parameter uncertainty analysis, n are greater than or equal to 2；
(4) monitoring data of the value of m group model parameter, the monitoring data of boundary river and lake itself are substituted into water quality model
It is calculated, determines the m group value of objective function；
(5) compare the corresponding parameter set that is dominant of Pareto set obtained under objective function, parameter is dominant the parameter group number d of concentration
Not less than 500；Increase the parameter group number m extracted in step (2) if d is less than 500, and repeats step (2)-(4)；
(6) the two-parameter joint probability density function for drawing the parameter set that is dominant, carries out two-parameter analysis of uncertainty；
(7) using kernel function estimation and each parameter is drawn in the probability density distribution of concentration that is dominant, probability density highest point
Corresponding value is as parameter rating of the model result；
(8) monitoring data of boundary river needed for predicting are collected and the model parameter of calibration substitutes into water quality model together, into
Row water quality prediction.
2. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that in the step (1):
A. the lake for the water quality predicted collected and its monitoring data of boundary river are 2 months at least continuous, at least average every
Week 1 time, monitoring data include the water monitoring data in predicted lake, the water monitoring data of lake boundary river and boundary
The data on flows in river；
B. according to the water quality indicator for including in the lake for the water quality predicted collected and its monitoring data of boundary river and
The index predicted determines the model for predicting lake water quality, and the parameter of the model is determined according to its model structure.
3. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (2) specifically includes the following steps:
A. the value range and its distribution of model parameter are determined, it can be assumed that model parameter is equal in its respective value range
Even distribution；
B. according to its distribution form in the respective value range of each parameter, m random number is generated with random number generator,
M need to be more than or equal to 10000；
C. the stochastic parameter value of generation is grouped according to model needs, it is ensured that each parameter has and only 1 in every group
Value, forming m group altogether can be used for the parameter group of driving model.
4. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (3) specifically includes the following steps:
A. water quality prediction is required to analyze；
B. according to analysis requirement result, determine that n objective function for carrying out parameter uncertainty analysis, n are greater than or equal to 2；Mesh
Scalar functions can be the average relative error RE of the pattern die analog values S and monitor value O of varying environment index and theirs is square
Root error RMSE, is specifically shown in following two formula:
Wherein OiIt is i-th of monitor value, SiIt is and OiCorresponding pattern die analog values, T are monitor value sums.
5. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (4) specifically includes the following steps:
A. the value of m group model parameter, the monitoring data of corresponding boundary river are substituted into water quality model and calculated, determine m
The analogue value S of each environmental index of groupi, i=1,2,3...T；
B. the monitoring data O in lake itself is combinedi, i=1,2,3...T, calculate the value of every group of objective function, total m group.
6. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (5) specifically includes the following steps:
A. objective function property is distinguished, is classified as that the bigger representative simulation result of objective function is better and objective function smaller generation
The better two major classes of table simulation result；Negative sign will be added before a kind of objective function that objective function is the bigger the better, makes the two classification offers of tender
It is better that number is unified for the smaller representative simulation result of objective function；
If b. defining the corresponding n objective function of parameter group u is both greater than the corresponding n objective function of parameter group v；Then think parameter
Group u is dominant by parameter group v；
C. compare the corresponding objective function of m group parameter, wherein all define in above-mentioned steps b lower is dominant by arbitrarily group parameter
Parameter group removal；The collection of remaining d group target function value is collectively referred to as Pareto set, and d is not less than 500；
D. the corresponding parameter group of Pareto set is extracted, d group parameter is obtained and forms the parameter set that is dominant；
E. increase the parameter group number m extracted in step (2) if d is less than 500, and repeat step (2)-(4).
7. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (6) specifically includes the following steps:
A. the two-parameter joint probability density function for the parameter set that is dominant is drawn；
B. carry out two-parameter analysis of uncertainty with ocular estimate: the big local parameter uncertainty of probability density is small, probability density
Small local parameter uncertainty is big.
8. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (7) specifically includes the following steps:
A. using kernel function estimation and each parameter is drawn in the probability density distribution of concentration that is dominant；Its Kernel Function is using high
This kernel function；
B. the corresponding value of select probability density highest point is as parameter rating of the model result.
9. a kind of side for carrying out lake water quality model prediction by multiple target analysis of uncertainty according to claim 1
Method, which is characterized in that the step (8) specifically includes the following steps:
A. the model parameter that calibration is completed is arranged；
B. the monitoring data of boundary river needed for predicting are collected and the model parameter of calibration substitutes into water quality model together, are carried out
Water quality prediction.
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Cited By (1)
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| CN114169242A (en) * | 2021-12-10 | 2022-03-11 | 浙江嘉科新能源科技有限公司 | Intelligent control algorithm for analyzing ecological oxygenation of river channel based on parameter uncertainty |
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