CN116108745A - Multi-parameter calibration method for water environment model, terminal equipment and storage medium - Google Patents
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Abstract
The invention discloses a multi-parameter calibration method, terminal equipment and storage medium of a water environment model, which comprises the steps of selecting calibration parameters, extracting parameter combinations of a set number, and taking the parameter combinations as input of an SWMM model to obtain output results of the set number; constructing a training set by using the output result, taking the training set as input of a neural network model, and training the neural network model; initializing population scale, iteration times and variation probability, setting particle dimensions, taking the parameter combination as input of a differential evolution algorithm, and iterating according to an objective function until the set iteration times are reached, so as to obtain a final parameter combination. According to the invention, by utilizing the parameter samples trained by the neural network, the further optimization calibration of the model parameters is realized by means of a differential evolution algorithm, the global optimal purpose of parameter searching is achieved, and the problems that the model is different in parameter co-efficiency and dynamic change parameters are difficult to calibrate are effectively solved.
Description
Technical Field
The invention relates to the field of parameter calibration of water environment models, in particular to a multi-parameter calibration method, terminal equipment and a storage medium of a water environment model.
Background
The water environment model is an important means for researching water resources and hydrologic phenomena, and the water environment problem is increasingly serious along with the influence of climate change and human activities at present. Therefore, in order to better study the water environment problem, the parameters of the water environment model are accurately calibrated, so that the model can better reflect the actual problem, and the method has important practical significance.
The existing parameter calibration method has the following problems when applied to a water environment model: (1) The traditional water environment model parameter trial-error calibration method has low intelligent degree, is lower in efficiency and long in time consumption compared with the manual calibration method depending on manual experience, and cannot take specific values of parameters for different simulation objects; (2) The existing common intelligent automatic calibration method has low precision, and the multi-parameter global optimization is difficult to achieve, so that the homonymy phenomenon (the homonymy phenomenon refers to that different parameter combinations are input into an SWMM model, and the simulation results of the finally obtained models have little or the same difference); (3) Many intelligent algorithms are prone to over-fitting problems at present and are cumbersome to rate for parameters that change dynamically over time.
CN110633532a provides a high-precision calibrating method for SWMM model parameters, the method optimizes parameter combinations by adopting a particle swarm optimization method, the objective function considers the data of all monitoring water outlets, the workload is large, the calibrating effect is poor easily caused by abnormality of certain measuring point data, and meanwhile, compared with the application of a differential evolution algorithm in parameter optimization, the particle swarm algorithm has poor convergence and the parameter optimization precision is still to be improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-parameter calibration method, terminal equipment and storage medium for a water environment model, which improve the parameter calibration precision; and realizing reasonable evaluation of the multi-parameter calibration global optimum and the calibration model.
In order to solve the technical problems, the invention adopts the following technical scheme: a multi-parameter calibration method for a water environment model comprises the following steps:
s1, selecting a Manning coefficient of a water impermeable area, a Manning coefficient of the water permeable area, a depression accumulation amount of the water impermeable area, a depression accumulation amount of the water permeable area, a minimum infiltration rate and a decay rate constant as calibration parameters, extracting a set number of parameter combinations, and taking the parameter combinations as input of an SWMM model to obtain an output result of the set number;
s2, constructing a training set by using the output result, taking the training set as input of a neural network model, and training the neural network model;
s3, setting the following objective function NSE:
wherein ,
for the actual observed peak flow at time t, +.>
For the simulated peak flow of the trained neural network model output in the region studied at time t,/I>
For the actual observed production value at time t, +.>
Outputting a simulated current value for the trained neural network model in the researched area; />
and />
The average value of the observed peak value and the observed yield value of the model is obtained; w is the weight of the peak flow index; t is the time sequence length of the simulation value output by the SWMM model or the time sequence length of the observation value data corresponding to the simulation value;
initializing population scale, iteration times and variation probability, setting particle dimensions, taking the parameter combination as input of a differential evolution algorithm, and iterating with the objective function approaching 1 as a target until the set iteration times are reached, so as to obtain a final parameter combination.
The invention inputs the extracted parameter sample sequence into the SWMM model to obtain the model output value, selects the total yield flow and the peak flow value from a plurality of output values as the output result, then puts the input and output values into the built BP neural network to complete the network training, establishes the mapping relation from input to output, inputs the network learning sample as a differential evolution algorithm, leads the algorithm to follow the multi-objective function calibration rule according to the global optimal searching capability, and realizes the calibration optimization of SWMM model parameters. The invention improves the precision of parameter calibration, realizes the overall optimization of multiple parameters, and solves the problems of synchronous efficiency of the model and difficult calibration of dynamic change parameters.
In order to further improve the parameter calibration precision, the method of the invention further comprises the following steps: inputting the final parameter combination into the SWMM model to obtain total yield and peak flow in an output result of the SWMM model, comparing the total yield and the peak flow with actual total yield and peak flow respectively, and judging whether the final parameter combination meets the precision requirement.
And when the final parameter combination does not meet the precision requirement, returning to the step S1.
In order to effectively improve the accuracy and precision of parameter calibration, in step S1 of the present invention, the value range of each calibration parameter is divided into M intervals, and a latin hypercube sampling mode is adopted to extract a value from each interval, so as to obtain M data sequences, namely M parameter combinations. In the invention, in order to ensure that the number of training samples of the neural network is enough, M is more than or equal to 600.
In step S2, a data set is constructed by selecting the peak flow and the total production flow data in the output result, and the data set is randomly divided into a training set and a verification set.
In order to further improve the calibration accuracy, in the present invention, step S2 further includes: and verifying the accuracy of the trained neural network model by using the verification set.
The ratio of the number of samples in the training set to the number of samples in the validation set is 7:3.
As an inventive concept, the present invention also provides a terminal device including a memory, a processor, and a computer program stored on the memory; the processor executes the computer program to implement the steps of the above-described method of the present invention.
A computer readable storage medium having stored thereon computer programs/instructions; the computer program/instructions, when executed by a processor, implement the steps of the above-described method of the present invention.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention establishes the corresponding relation between the rating parameter and the model result based on Latin Hypercube (LHS) sampling mode and the neural network method, thereby effectively improving the precision and accuracy of parameter rating;
2. according to the invention, by utilizing the parameter samples trained by the neural network, the further optimization calibration of the model parameters is realized by means of a differential evolution algorithm, the global optimal purpose of parameter searching is achieved, and the problems that the model is different in parameter co-efficiency and dynamic change parameters are difficult to calibrate are effectively solved.
Drawings
FIG. 1 is a flow chart for establishing a sample mapping relationship between calibration parameters and SWMM model output results according to an embodiment of the present invention;
FIG. 2 is a flow chart of optimizing parameter combinations using a differential evolution algorithm according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this document, the terms "comprise," "include," and other similar words are intended to denote a logical relationship, but not to be construed as implying a spatial structural relationship. For example, "a includes B" is intended to mean that logically B belongs to a, and not that spatially B is located inside a. In addition, the terms "comprising," "including," and other similar terms should be construed as open-ended, rather than closed-ended. For example, "a includes B" is intended to mean that B belongs to a, but B does not necessarily constitute all of a, and a may also include other elements such as C, D, E.
Example 1
As shown in fig. 1 and fig. 2, the present embodiment provides a multi-parameter calibration method for a water environment model, including:
1) Establishing a rating parameter sample: for the SWMM model, the actual needs can be met by empirically determining the value of the parameter with lower sensitivity, and the parameters with 6 requirements of the Manning coefficient of the impermeable zone, the Manning coefficient of the permeable zone, the depression accumulation of the impermeable zone, the depression accumulation of the permeable zone, the minimum infiltration rate and the attenuation rate, which are higher in sensitivity to the output result, are selected, 600 intervals are divided according to the value range of each parameter, then 600 parameter combination samples are extracted by using a Latin Hypercube (LHS) sampling mode and input into the SWMM model, and 600 output results corresponding to the monitoring data are obtained.
In this embodiment, for 6 rated parameters with higher sensitivity, the parameters are equally divided into 600 intervals according to a value range, a value is extracted in each interval by using a Latin Hypercube (LHS) sampling mode, and one value in each parameter is sequentially selected to form a data sequence with the length of 6, so that 600 data sequences are formed in total.
2) Training and verifying a BP neural network model: establishing a BP neural network model, setting the number of input nodes as 6, setting the number of output nodes as 2, setting a training set and a verification set of the neural network to account for 70% and 30% of the total 600 data sets, taking 600 calibration parameter values as network inputs and 2 corresponding results as outputs, performing network model training, and establishing a mapping relation between the calibration parameters and peak flow and total flow to obtain a high-precision nonlinear neural network mathematical model. And after the network training is completed, storing the fitted network result.
In the embodiment, 600 data sequences are sequentially input into an established SWMM model to obtain corresponding 600 model output results, and peak flow and total production flow data are selected from a plurality of results to form corresponding samples; and distributing 600 samples according to the ratio of training set/verification set=7:3, completing training and testing of the model, and constructing a high-precision corresponding relation between parameters and model output values.
3) Multi-objective rating optimization: and (3) introducing a differential evolution algorithm to perform rating optimization, taking the improved multi-objective Nash efficiency coefficient NSE as a rating objective function, selecting a multi-objective function constructed by two indexes of peak flow and total flow as an fitness function in the differential evolution program to perform parameter rating, and enhancing the capability of the algorithm for parameter global optimal search rating. In this embodiment, the objective function for optimizing the differential evolution algorithm is calculated by using the following formula:
in the formula ：
is the actual observed peak flow (m 3 /s)；
w is the weight of the peak flow index of the two indexes, and is generally 0.5.
4) Differential evolution algorithm parameter setting: for the differential evolution algorithm for parameter calibration, parameters such as an initialized population size size_pop, iteration times max_iter, mutation probability and the like are reasonably set according to specific conditions, particle dimensions are set to be 6 according to calibration parameter values, and a series of parameters such as upper and lower parameter limits and the like are set.
In the embodiment, the minimum error between the monitoring data and the operation result data of the corresponding neural network model is used as an objective function, a high-precision nonlinear mathematical model is utilized, a differential evolution algorithm is adopted to invert to obtain a model parameter value, and the finally obtained parameter is substituted into a specific SWMM project model for verification, so that the precision condition of the model parameter value is verified and analyzed. In the differential evolution algorithm program, the population is iterated continuously according to the requirement that the objective function value is approximately 1, after a certain number of steps, the algorithm can achieve convergence, and the corresponding parameter value is the final rating parameter value of the project model. After the model parameter value is determined, a certain working condition is selected, the parameter is input into the SWMM model, the total yield flow and the peak flow in the model output result are obtained, the total yield flow and the peak flow are compared with the measured value, the precision of the differential evolution algorithm is verified, and the final rating parameter combination is determined.
Example 2
Embodiment 2 of the present invention provides a terminal device corresponding to embodiment 1, where the terminal device may be a processing device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method of the embodiment.
The terminal device of the present embodiment includes a memory, a processor, and a computer program stored on the memory; the processor executes the computer program on the memory to implement the steps of the method of embodiment 1 described above.
In some implementations, the memory may be high-speed random access memory (RAM: random Access Memory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other implementations, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general-purpose processor, which is not limited herein.
Example 3
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the preceding.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (9)
1. The multi-parameter calibration method for the water environment model is characterized by comprising the following steps of:
s1, selecting a Manning coefficient of a water impermeable area, a Manning coefficient of the water permeable area, a depression accumulation amount of the water impermeable area, a depression accumulation amount of the water permeable area, a minimum infiltration rate and a decay rate constant as calibration parameters, extracting a set number of parameter combinations, and taking the parameter combinations as input of an SWMM model to obtain an output result of the set number;
s2, constructing a training set by using the output result, taking the training set as input of a neural network model, and training the neural network model;
s3, setting the following objective function NSE:
wherein ,
for the actual observed peak flow at time t, +.>
For the simulated peak flow of the trained neural network model output in the region studied at time t,/I>
For the actual observed production value at time t, +.>
Outputting a simulated current value for the trained neural network model in the researched area; />
and />
The average value of the observed peak value and the observed yield value of the model is obtained; w is the weight of the peak flow index; t is the time sequence length of the simulation value output by the SWMM model or the time sequence length of the observation value data corresponding to the simulation value;
initializing population scale, iteration times and variation probability, setting particle dimensions according to the number of the rated parameters, taking the parameter combination as input of a differential evolution algorithm, and iterating with the objective function approaching 1 as a target until the set iteration times are reached, so as to obtain the final parameter combination.
2. The method for multi-parameter calibration of a water environment model of claim 1, further comprising: inputting the final parameter combination into the SWMM model to obtain total yield and peak flow in an output result of the SWMM model, comparing the total yield and the peak flow with actual total yield and peak flow respectively, and judging whether the final parameter combination meets the precision requirement.
3. The method for calibrating multiple parameters of a water environment model according to claim 2, wherein when the final parameter combination does not meet the accuracy requirement, the method returns to step S1.
4. The multi-parameter calibration method of water environment model according to claim 1, wherein in step S1, the value range of each calibration parameter is divided into M intervals, and a latin hypercube sampling mode is adopted to extract a value from each interval, so as to obtain M data sequences, namely M parameter combinations.
5. The method for calibrating multiple parameters of a water environment model according to claim 1, wherein in step S2, a data set is constructed by selecting peak flow and total flow data in the output result, and the data set is randomly divided into a training set and a verification set.
6. The method for calibrating multiple parameters of a water environment model according to claim 5, wherein in step S2, the method further comprises: and verifying the accuracy of the trained neural network model by using the verification set.
7. The method of multi-parameter calibration of a water environment model of claim 5, wherein the ratio of the number of samples in the training set to the number of samples in the validation set is 7:3.
8. A terminal device comprising a memory, a processor and a computer program stored on the memory; characterized in that the processor executes the computer program to carry out the steps of the method according to one of claims 1 to 7.
9. A computer readable storage medium having stored thereon computer programs/instructions; characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method according to one of claims 1 to 7.
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| CN116401749B (en) * | 2023-06-09 | 2023-09-01 | 吉林大学 | Method, system, electronic equipment and storage medium for determining circulation well parameters |
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