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Related Experiment Video

Updated: Jul 30, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Two-step approach based multi-objective groundwater remediation using enhanced random vector functional link

Partha Majumder1, Chunhui Lu1, T I Eldho2

  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China.

Journal of Contaminant Hydrology
|May 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-step simulation-optimization model for groundwater remediation. The enhanced random vector functional link and evolutionary marine predator algorithm significantly improve contaminant mass reduction and pumping efficiency.

Keywords:
Enhanced random vector functional link (ERVFL)Evolutionary marine predator algorithm (EMPA)Groundwater remediationMulti-objective optimization

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Area of Science:

  • Environmental Engineering
  • Computational Hydrogeology
  • Optimization Techniques

Background:

  • Groundwater contamination poses significant environmental and health risks, necessitating efficient remediation strategies.
  • Traditional multi-objective groundwater remediation methods often face computational challenges and limitations in achieving optimal solutions.
  • Accurate modeling of groundwater flow and solute transport is crucial for effective remediation planning.

Purpose of the Study:

  • To propose a novel two-step simulation-optimization model for multi-objective groundwater remediation.
  • To enhance computational performance and robustness of groundwater models using enhanced random vector functional link (ERVFL) networks.
  • To develop an improved evolutionary marine predator algorithm (EMPA) for efficient multi-objective optimization.

Main Methods:

  • Development of groundwater flow and solute transport models using MODFLOW and MT3DMS.
  • Approximation of flow and transport models using ERVFL networks with kernel density estimator (KDE) based weighted least squares for improved robustness.
  • Enhancement of the marine predator algorithm (MPA) through elite opposition-based learning, biological evolution operators, and elimination mechanisms for multi-objective optimization.
  • Implementation of a two-step approach: Step 1 optimizes pumping well locations, and Step 2 uses an ERVFL-based proxy model with EMPA for multi-objective optimization.

Main Results:

  • The ERVFL network significantly improved the computational performance of the groundwater flow and solute transport models.
  • The enhanced EMPA demonstrated superior performance in finding Pareto-optimal solutions for multi-objective groundwater remediation.
  • The two-step approach effectively identified optimal pumping well locations and provided a Pareto-optimal front illustrating the trade-off between pumping rate and contaminant mass reduction.
  • The proposed methodology showed a significant advantage over traditional methods in achieving groundwater remediation goals.

Conclusions:

  • The developed two-step simulation-optimization model offers an efficient and robust approach for multi-objective groundwater remediation.
  • The integration of ERVFL networks and the enhanced EMPA provides a powerful tool for addressing complex groundwater contamination challenges.
  • This study highlights the potential of advanced computational techniques in improving the effectiveness and efficiency of environmental management strategies.