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Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method-A Case Study of Western Jilin Province.

An Y, Lu W, Cheng W - Int J Environ Res Public Health (2015)

Bottom Line: Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy.The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days.The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China. anyongkai1991@163.com.

ABSTRACT
This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.

No MeSH data available.


Related in: MedlinePlus

Process of the genetic algorithm.
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ijerph-12-08897-f003: Process of the genetic algorithm.

Mentions: The genetic algorithm (GA) is a computational model on the basis of Darwin's biological evolution theory genetic mechanism, used to search for the optimal solution by simulating natural evolution. It includes three genetic operators of selection, crossover and mutation [48,49,50]. A flowchart for solving a general problem through the genetic algorithm is shown in Figure 3.


Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method-A Case Study of Western Jilin Province.

An Y, Lu W, Cheng W - Int J Environ Res Public Health (2015)

Process of the genetic algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555255&req=5

ijerph-12-08897-f003: Process of the genetic algorithm.
Mentions: The genetic algorithm (GA) is a computational model on the basis of Darwin's biological evolution theory genetic mechanism, used to search for the optimal solution by simulating natural evolution. It includes three genetic operators of selection, crossover and mutation [48,49,50]. A flowchart for solving a general problem through the genetic algorithm is shown in Figure 3.

Bottom Line: Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy.The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days.The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China. anyongkai1991@163.com.

ABSTRACT
This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.

No MeSH data available.


Related in: MedlinePlus