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Groundwater contaminant source identification using swarm intelligence-based simulation optimization models.

K Swetha1, T I Eldho2, L Guneshwor Singh3

  • 1Homi Bhabha National Institute (HBNI), Mumbai, India.

Environmental Science and Pollution Research International
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a linked simulation optimization model for pinpointing groundwater contaminant sources. The LRPIM-TLBO model demonstrated superior accuracy in identifying contaminant sources and release histories compared to LRPIM-PSO and LRPIM-GWO.

Keywords:
GWOLocal radial point interpolation method (LRPIM)Meshless methodOptimizationPSOSource identificationTLBO

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Computational Modeling

Background:

  • Groundwater contamination poses significant environmental and health risks.
  • Accurate identification of contaminant sources is crucial for effective remediation strategies.
  • Existing methods for source identification often face challenges with complex aquifer systems.

Purpose of the Study:

  • To develop and evaluate a linked simulation optimization (SO) model for groundwater contaminant source identification (SI).
  • To integrate the meshless Local Radial Point Interpolation Method (LRPIM) with swarm intelligence optimization algorithms.
  • To compare the performance of LRPIM-TLBO, LRPIM-PSO, and LRPIM-GWO for SI.

Main Methods:

  • A simulation model based on the advection-dispersion-reaction equation (ADRE) was developed using the LRPIM.
  • The LRPIM simulation model was coupled with Teaching-Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO).
  • The SO models were applied to hypothetical and real aquifer problems to identify contaminant source locations and release histories by minimizing prediction-observation discrepancies.

Main Results:

  • All three developed SO models (LRPIM-TLBO, LRPIM-PSO, LRPIM-GWO) successfully identified groundwater contaminant sources and their release histories.
  • The LRPIM-TLBO model exhibited the highest accuracy in source identification.
  • LRPIM-PSO and LRPIM-GWO also provided satisfactory results, with LRPIM-PSO being more accurate than LRPIM-GWO.

Conclusions:

  • Linked simulation optimization models are effective tools for groundwater contaminant source identification.
  • The LRPIM-TLBO approach offers a highly accurate method for determining contaminant source parameters.
  • The study highlights the potential of swarm intelligence algorithms in addressing complex hydrogeological challenges.