Limits...
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

The actual and computed equipotential lines of groundwater table at the end of the model calibration stage.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4555255&req=5

ijerph-12-08897-f006: The actual and computed equipotential lines of groundwater table at the end of the model calibration stage.

Mentions: The calibration phase of simulation was selected in the dry season for 181 days from October 1, 2006 to March 31, 2007, taking into consideration that less source and sink are beneficial to identify hydrogeology parameters. The verification phase was selected in the wet season for 182 days from April 1, 2007 to September 30, 2007, on account that more source and sink are beneficial to verify the effectiveness of hydrogeology parameters. The fitting results of computed groundwater table and the actual measured groundwater table are shown in Figure 4 and Figure 5 at the end of the model calibration and verification stage respectively. The equipotential lines of the groundwater table are also shown in Figure 6 and Figure 7 at the end of the model calibration and verification stage respectively.


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)

The actual and computed equipotential lines of groundwater table at the end of the model calibration stage.
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-12-08897-f006: The actual and computed equipotential lines of groundwater table at the end of the model calibration stage.
Mentions: The calibration phase of simulation was selected in the dry season for 181 days from October 1, 2006 to March 31, 2007, taking into consideration that less source and sink are beneficial to identify hydrogeology parameters. The verification phase was selected in the wet season for 182 days from April 1, 2007 to September 30, 2007, on account that more source and sink are beneficial to verify the effectiveness of hydrogeology parameters. The fitting results of computed groundwater table and the actual measured groundwater table are shown in Figure 4 and Figure 5 at the end of the model calibration and verification stage respectively. The equipotential lines of the groundwater table are also shown in Figure 6 and Figure 7 at the end of the model calibration and verification stage respectively.

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