Groundwater Remediation Design Underpinned By Coupling Evolution Algorithm With Deep Belief Network Surrogate

Yu Chen (), Guodong Liu (), Xiaohua Huang and Yuchuan Meng
Additional contact information
Yu Chen: Sichuan University
Guodong Liu: Sichuan University
Xiaohua Huang: Sichuan University
Yuchuan Meng: Sichuan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 7, No 6, 2223-2239

Abstract: Abstract Groundwater remediation design is crucial to contemporary water resources management, which is prone to massive computational costs due to the complexity and nonlinearity of the groundwater system. Traditional surrogate methods that can reduce the computational costs tend to encounter barriers of scalability and accuracy when the input–output relationship is highly nonlinear or high-dimensional. To tackle these problems, we herein propose a novel simulation–optimization method that embeds the deep learning deep belief network (DBN) into the particle swarm optimization (PSO) algorithm for groundwater remediation design. Firstly, a numerical simulation model based on MODFLOW and MT3DMS is established to describe the impact on the pollution environmental fate of various implementations of the remediation strategy. The input dataset to train DBN is comprised of various remediation strategies that evolve automatically in the PSO iterations, and the corresponding output dataset constituted of contaminant concentration at observation wells is garnered by executing the simulation model. In the optimization process, the DBN is retrained in an adaptive pattern to enhance prediction accuracy, selectively substituting for the original simulation model to alleviate the computational burden. Additionally, the PSO algorithm undergoes discretization and collision averting within each individual to adapt to the specific remediation task. The results reveal that the proposed method manifests satisfactory convergence behaviour and accuracy, capable of unburdening a considerable proportion (68.8%) of the time consumption for optimal groundwater remediation design.

Keywords: Surrogate model; Deep belief network; Groundwater remediation; Deep learning; PSO algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03137-w 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:7:d:10.1007_s11269-022-03137-w

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

DOI: 10.1007/s11269-022-03137-w

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 ().