An Adaptive Dynamic Kriging Surrogate Model for Application to the Optimal Remediation of Contaminated Groundwater

Shuangsheng Zhang, Jing Qiang (), Hanhu Liu, Xiaonan Wang, Junjie Zhou and Dongliang Fan
Additional contact information
Shuangsheng Zhang: Xuzhou University of Technology
Jing Qiang: China University of Mining and Technology
Hanhu Liu: China University of Mining and Technology
Xiaonan Wang: Zhejiang University of Technology
Junjie Zhou: Xuzhou University of Technology
Dongliang Fan: Xuzhou University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 13, No 9, 5032 pages

Abstract: Abstract When using the simulation–optimization model to optimize groundwater extraction-treatment schemes, constructing a surrogate model for the numerical simulation model is an effective tool for overcoming the large computational load. However, the construction of a one-shot static-surrogate model has disadvantages, such as a large sample size, low accuracy, and the problem of losing the optimal solution. A construction strategy for a batch locally optimal solution-based adaptive dynamic kriging surrogate model is proposed here and applied to the optimal remediation of contaminated groundwater. First, the preliminary kriging surrogate model is established by the kriging method. Second, the adaptive dynamic kriging surrogate model is updated based on the batch locally optimal solutions method. Finally, when the accuracy of the adaptive dynamic kriging surrogate model reaches the convergence criterion, the update stops to obtain the convergent adaptive kriging surrogate model and the optimal remediation scheme. The results show that the optimal pumping wells based on the convergent adaptive kriging surrogate model are well 5, well 6, and well 9, with a remediation cost of ¥ 44,336.16. All pollutant concentrations meet the limit (6 mg/L). This remediation scheme has better effects and less costs than that based on the preliminary kriging surrogate model. Therefore, the batch locally optimal solution-based convergent adaptive kriging surrogate model can effectively avoid the risk of losing the optimal solution, which is of great importance for improving the computational efficiency and accuracy of solving the simulation–optimization model.

Keywords: Extraction and treatment; Kriging surrogate model; Genetic algorithm; Locally optimal solution; Adaptive sampling method (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03289-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03289-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-022-03289-9

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().