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

• Published in: Water resources management Vol. 36; no. 7; pp. 2223 - 2239
• Main Authors: Chen, Yu, Liu, Guodong, Huang, Xiaohua, Meng, Yuchuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2022; Springer Nature B.V
• Subjects: Accuracy; administrative management; Algorithms; Atmospheric Sciences; Belief networks; Civil Engineering; Computer applications; Computer simulation; Computing costs; Contaminants; data collection; Datasets; Deep learning; Design; Earth and Environmental Science; Earth Sciences; Environment; environmental fate; Evolutionary algorithms; Geotechnical Engineering & Applied Earth Sciences; Groundwater; Groundwater treatment; Hydrogeology; hydrologic models; Hydrology/Water Resources; Machine learning; Mathematical models; Methods; Nonlinear systems; Nonlinearity; Observation wells; Optimization; Particle swarm optimization; Pollution; prediction; Remediation; Simulation; simulation models; Water resources; Water resources management; Deep learning; Groundwater remediation; Surrogate model; Deep belief network; PSO algorithm
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-022-03137-w

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.