CN116757098B - Automatic verification method based on SWAT model multi-objective optimization - Google Patents
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Abstract
The invention discloses an automatic verification method based on SWAT model multi-objective optimization, which comprises the following steps: s1, initializing a verification program of a SWAT model; s2, setting verification parameters, wherein the specific setting process is as follows: s21, setting an optimization algorithm, the initial population number, the iteration number, the sub population number and the cross variation value of the optimization algorithm, and setting the process number; s22, setting a parameter set range for verification and a parameter adjustment mode of each parameter in the model verification process, wherein the parameter adjustment mode comprises a relative change rate method, an absolute change rate method and a replacement rate method; s3, iteratively operating the SWAT model; s4, evaluating the model by using an optimization algorithm; s5, running the SWAT model for checking completion; the invention can perform multi-objective optimization and multi-site verification, improves the verification efficiency, and solves the problems of identical parameter and different parameter of the model.
Description
Technical Field
The invention relates to the technical field of computer processing, in particular to an automatic verification method based on SWAT model multi-objective optimization.
Background
The SWAT (Soil and Water Assessment Tool) model is one of the most widely used hydrologic water quality models at present. The model has obvious effects on hydrologic water quality simulation, watershed treatment scene analysis and the like, and is helpful for watershed management personnel to understand the circulating process of watershed hydrologic water quality and to make a watershed management scheme. However, depending on the setup of the SWAT model, it may have thousands or more of parameters that need to be verified before they can be applied to the actual study. The verification method of the SWAT model is generally divided into two methods of manual calibration and automatic calibration. Manual calibration requires a user to have high expertise and is very familiar with the model, and because of the numerous parameters of the SWAT model, it takes a lot of time and effort to verify the model using manual calibration. The essence of automatic verification is a trial-and-error method, the verification process is iterated continuously through a computer, the fitting degree of the simulation data and the monitoring data is optimized, and finally a group of parameter combinations which enable the fitting degree to be better are found. Along with the development of computer technology and artificial intelligence, the optimization methods applied to the verification of the hydrological water quality model at present mainly comprise SUFI-2, POS, NSGA3, SA and the like, and the methods are integrated into software such as SWAT-CUP, R-SWAT and the like at present. These algorithms and software, while speeding up the verification process of the SWAT model, have three drawbacks: (1) The use of these algorithms does not solve the problems of model homotopy and homotopy; (2) The two software integrated algorithms are single-target or double-target optimization algorithms, and the algorithms can have the problems of low convergence speed and the like when dealing with the multi-site model verification task; (3) Software generally uses a multithreading mode to accelerate the model verification speed, but the SWAT model is a computation intensive model, and the model verification speed can be more effectively accelerated by adopting a multiprocessing model iteration mode. Therefore, the verification process of the SWAT model has a large improvement space, and still needs to be further optimized to meet the actual use demands of people. Accordingly, the invention discloses an automatic verification method of the SWAT model, which fuses the contents of SWAT model parameter modification, a multi-objective optimization algorithm, a multi-process operation mode and the like. The method can provide a plurality of sets of model parameter schemes for users, and solves the problems of model homonymy and homonymy while improving the model verification efficiency.
Disclosure of Invention
The invention aims to provide an automatic verification method based on SWAT model multi-objective optimization, which fuses the contents of SWAT model parameter modification, multi-objective optimization algorithm, multi-process operation mode and the like, provides a multi-set model parameter scheme for a user, can perform multi-objective optimization, multi-site verification, improves verification efficiency and solves the problems of model co-parameter and co-parameter.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an automatic verification method based on SWAT model multi-objective optimization comprises the following steps:
s1, initializing a verification program of a SWAT model, constructing a framework structure for coupling the SWAT model with a multi-objective optimization method, and under the framework, applying the multi-objective optimization method to the verification of the SWAT model, wherein the specific initialization process is as follows:
s11, copying an input/output folder of the constructed SWAT model from the ArcSWAT model;
s12, determining hydrologic water quality indexes for verification, and creating an EXCEL file of monitoring data of the corresponding indexes;
s13, exporting a position table in the SWAT model to a location_table folder;
s2, setting verification parameters, wherein the specific setting process is as follows:
s21, setting an optimization algorithm, the initial population number, the iteration number, the sub population number and the cross variation value of the optimization algorithm, and setting the process number;
s22, setting a parameter set range for verification and a parameter adjustment mode of each parameter in the model verification process, wherein the parameter adjustment mode comprises a relative change rate method, an absolute change rate method and a replacement rate method;
the calculation formula of the relative change calibration method is as follows:，
the calculation formula of the absolute change calibration method is as follows:，
the calculation formula of the replacement rating method is as follows:，
wherein,is a modified parameter value,/->Is the original value of the parameter in the SWAT model, x is the parameter variation factor;
s3, iteratively operating the SWAT model;
s4, evaluating the model by using an optimization algorithm;
s5, running the SWAT model for checking completion.
Preferably, the location table in step S13 includes a hru table, an rch table and a sub table.
Preferably, step S3 performs iterative operation on the SWAT model according to the initial population and the number of sub-populations of each generation set in step S21, and specifically includes the following steps:
s31, acquiring parameter samples from the parameter set range in the step S22 by using a sampling method according to the initial population quantity or the sub population quantity, and generating a parameter table set;
s32, performing iterative operation on the SWAT model according to the number of processes set in the step S21.
Preferably, the sampling method in step S31 is random sampling, binomial random sampling or super-pulling Ding Lifang body sampling; the specific process of step S32 is:
s321, corresponding the parameter table in the step S31 to the position table in the step S13, and generating a position parameter table to be modified;
s322, selecting corresponding files from the input/output folders according to the position parameter table, and storing the files in the memory;
s323, modifying files to be modified in the position parameter table by using different functions according to the attribute of each file, and importing the files which are modified and kept in the present state into an operation folder to perform model simulation;
s324, after the SWAT model in the operation folder is operated, firstly, restoring the model file in the operation folder to the original state of the model according to the files screened in the step S322, then, reading out the model result in the operation folder by using a reading file module, wherein the reading file comprises hydrological response unit data, sub-river basin data and river data, and storing the reading file, a parameter table and a position parameter table in an output folder together for carrying out fitting degree on calculation model simulation values and monitoring values with the monitoring data.
Preferably, the specific process of step S4 is:
s41, calculating a fitness index according to the EXCEL file of the monitoring data created in the step S12 and the simulation value read in the step S324, wherein the calculation formula of the fitness index is as follows: wherein (1)>Represents goodness of fit, ->The data of the i-th data is represented,representing Nash coefficient,/->Represents the ith monitored value,/-)>Mean value of all monitoring values, +.>Represents the i-th analog value,/, for>Representing an average value of the analog values;
s42, calculating all simulation results in the initial population and the sub population according to the fitting indexes of the step S41, and storing all fitting degree indexes in a memory;
s43, repeating the calculation process of the step S41 on all SWAT simulation results of the initial population and the sub population;
s44, repeating the steps S31-S42 according to the optimization algorithm and the iteration times set in the step S21, and screening non-dominant solutions from the initial population and the sub population;
and S45, when all iteration times operation is finished, calculating all non-dominant solutions and obtaining a Parteo optimal solution.
Preferably, the specific process of step S5 is:
s51, comparing parameter tables of SWAT models corresponding to all Parteo optimal solutions, and screening solutions meeting the watershed simulation conditions as final parameter sets of the models;
s52, substituting the final parameter set into the SWAT model to complete automatic verification of the model.
After the technical scheme is adopted, the invention has the following beneficial effects:
1. the invention can perform multi-objective optimization and multi-site verification, and various optimization algorithms (shown in table 1) are embedded in the SWAT model verification process, compared with the previous single-objective optimization algorithm, the invention can analyze the verification results aiming at a plurality of hydrologic water quality monitoring sites and various fitness evaluation indexes, and the optimization algorithms effectively optimize the verification process.
Optimization algorithm employed in Table 1
2. The invention can effectively improve the verification efficiency, adds a multi-process framework in the SWAT model, and can simulate a plurality of SWAT models by utilizing a computer CPU at the same time, so that the verification efficiency of the invention is higher than that of SWAT-CUP and R-SWAT.
3. The invention can effectively solve the problems of identical parameter and different parameter, adopts an optimization algorithm to check the SWAT model, returns a plurality of parameter sets, and can solve the problems of identical parameter and different parameter by comparing the simulation results of the parameter sets and combining with professional knowledge to judge the parameter set which is more in line with the watershed hydrologic water quality simulation condition.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a single SWAT model verification flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1 to 2, an automatic verification method based on SWAT model multi-objective optimization includes the following steps:
s1, initializing a verification program of a SWAT model, constructing a framework structure for coupling the SWAT model with a multi-objective optimization method, and under the framework, applying the multi-objective optimization method to the verification of the SWAT model, wherein the specific initialization process is as follows:
s11, copying an input/output folder of the constructed SWAT model from the ArcSWAT model;
s12, determining hydrologic water quality indexes for verification, and creating an EXCEL file of monitoring data of the corresponding indexes;
s13, exporting a position table in the SWAT model to a location_table folder;
the position table in the step S13 comprises a hru table, an rch table and a sub table;
s2, setting verification parameters, wherein the specific setting process is as follows:
s21, setting an optimization algorithm, the initial population number, the iteration number, the sub population number and the cross variation value of the optimization algorithm, and setting the process number;
s22, setting a parameter set range for verification and a parameter adjustment mode of each parameter in the model verification process, wherein the parameter adjustment mode comprises a relative change rate method, an absolute change rate method and a replacement rate method;
the calculation formula of the relative change calibration method is as follows:，
the calculation formula of the absolute change calibration method is as follows:，
the calculation formula of the replacement rating method is as follows:，
wherein,is a modified parameter value,/->Is the original value of the parameter in the SWAT model, x is the parameter variation factor;
s3, iteratively operating the SWAT model;
step S3, iteratively operating the SWAT model according to the initial population and the number of sub-populations of each generation set in the step S21, wherein the specific process is as follows:
s31, acquiring parameter samples from the parameter set range in the step S22 by using a sampling method according to the initial population quantity or the sub population quantity, and generating a parameter table set;
s32, performing iterative operation on the SWAT model according to the number of processes set in the step S21;
the sampling method in the step S31 is random sampling, binomial random sampling or superpulling Ding Lifang body sampling; the specific process of step S32 is:
s321, corresponding the parameter table in the step S31 to the position table in the step S13, and generating a position parameter table to be modified;
s322, selecting corresponding files from the input/output folders according to the position parameter table, and storing the files in the memory;
s323, modifying files to be modified in the position parameter table by using different functions according to the attribute of each file, and importing the files which are modified and kept in the present state into an operation folder to perform model simulation;
s324, after the SWAT model in the operation folder is operated, firstly recovering the model file in the operation folder to the original state of the model according to the files screened in the step S322, then reading out the model result in the operation folder by using a reading file module, wherein the reading file comprises hydrological response unit data, sub-river basin data and river data, and storing the reading file, a parameter table and a position parameter table in an output folder together for carrying out fitting degree on calculation model simulation values and monitoring values with the monitoring data;
s4, evaluating the model by using an optimization algorithm;
the specific process of step S4 is:
s41, calculating a fitness index according to the EXCEL file of the monitoring data created in the step S12 and the simulation value read in the step S324, wherein the calculation formula of the fitness index is as follows: wherein (1)>Represents goodness of fit, ->The data of the i-th data is represented,representing Nash coefficient,/->Represents the ith monitored value,/-)>Mean value of all monitoring values, +.>Represents the i-th analog value,/, for>Representing an average value of the analog values;
s42, calculating all simulation results in the initial population and the sub population according to the fitting indexes of the step S41, and storing all fitting degree indexes in a memory;
s43, repeating the calculation process of the step S41 on all SWAT simulation results of the initial population and the sub population;
s44, repeating the steps S31-S42 according to the optimization algorithm and the iteration times set in the step S21, and screening non-dominant solutions from the initial population and the sub population;
s45, when all iteration times operation is finished, calculating all non-dominant solutions and obtaining a Parteo optimal solution;
s5, running the SWAT model for checking completion;
the specific process of step S5 is:
s51, comparing parameter tables of SWAT models corresponding to all Parteo optimal solutions, and screening solutions meeting the watershed simulation conditions as final parameter sets of the models;
s52, substituting the final parameter set into the SWAT model to complete automatic verification of the model.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (2)
1. An automatic verification method based on SWAT model multi-objective optimization is characterized by comprising the following steps:
s1, initializing a verification program of a SWAT model, constructing a framework structure for coupling the SWAT model with a multi-objective optimization method, and under the framework, applying the multi-objective optimization method to the verification of the SWAT model, wherein the specific initialization process is as follows:
s11, copying an input/output folder of the constructed SWAT model from the ArcSWAT model;
s12, determining hydrologic water quality indexes for verification, and creating an EXCEL file of monitoring data of the corresponding indexes;
s13, exporting a position table in the SWAT model to a location_table folder;
the position table in the step S13 comprises a hru table, an rch table and a sub table;
s2, setting verification parameters, wherein the specific setting process is as follows:
s21, setting an optimization algorithm, the initial population number, the iteration number, the sub population number and the cross variation value of the optimization algorithm, and setting the process number;
s22, setting a parameter set range for verification and a parameter adjustment mode of each parameter in the model verification process, wherein the parameter adjustment mode comprises a relative change rate method, an absolute change rate method and a replacement rate method;
the calculation formula of the relative change calibration method is as follows:，
the calculation formula of the absolute change calibration method is as follows:，
the calculation formula of the replacement rating method is as follows:，
wherein,is a modified parameter value,/->Is the original value of the parameter in the SWAT model, x is the parameter variation factor;
s3, iteratively operating the SWAT model; step S3, performing iterative operation on the SWAT model according to the initial population quantity, the iterative times and the sub population quantity set in the step S21, wherein the specific process is as follows:
s31, acquiring parameter samples from the parameter set range in the step S22 by using a sampling method according to the initial population quantity or the sub population quantity, and generating a parameter table set;
s32, performing iterative operation on the SWAT model according to the number of processes set in the step S21;
the sampling method in the step S31 is random sampling, binomial random sampling or superpulling Ding Lifang body sampling; the specific process of step S32 is:
s321, corresponding the parameter table in the step S31 to the position table in the step S13, and generating a position parameter table to be modified;
s322, selecting corresponding files from the input/output folders according to the position parameter table, and storing the files in the memory;
s323, modifying files to be modified in the position parameter table by using different functions according to the attribute of each file, and importing the files which are modified and kept in the present state into an operation folder to perform model simulation;
s324, after the SWAT model in the operation folder is operated, firstly recovering the model file in the operation folder to the original state of the model according to the files screened in the step S322, then reading out the model result in the operation folder by using a reading file module, wherein the reading file comprises hydrological response unit data, sub-river basin data and river data, and storing the reading file, a parameter table and a position parameter table in an output folder together for carrying out fitting degree on calculation model simulation values and monitoring values with the monitoring data;
s4, evaluating the model by using an optimization algorithm;
the specific process of step S4 is:
s41, calculating a fitness index according to the EXCEL file of the monitoring data created in the step S12 and the simulation value read in the step S324, wherein the calculation formula of the fitness index is as follows: wherein (1)>Represents goodness of fit, ->The data of the i-th data is represented,representing Nash coefficient,/->Represents the ith monitored value,/-)>Mean value of all monitoring values, +.>Represents the i-th analog value,/, for>Representing an average value of the analog values;
s42, calculating all simulation results in the initial population and the sub population according to the fitting indexes of the step S41, and storing all fitting degree indexes in a memory;
s43, repeating the calculation process of the step S41 on all SWAT simulation results of the initial population and the sub population;
s44, repeating the steps S31-S42 according to the optimization algorithm and the iteration times set in the step S21, and screening non-dominant solutions from the initial population and the sub population;
s45, when all iteration times operation is finished, calculating all non-dominant solutions and obtaining a Parteo optimal solution;
s5, running the SWAT model for checking completion.
2. The automatic verification method based on the multi-objective optimization of the SWAT model as claimed in claim 1, wherein the specific process of the step S5 is as follows:
s51, comparing parameter tables of SWAT models corresponding to all Parteo optimal solutions, and screening solutions meeting the watershed simulation conditions as final parameter sets of the models;
s52, substituting the final parameter set into the SWAT model to complete automatic verification of the model.
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