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A comparative analysis of meshless based simulation optimization models with metaheuristic algorithms for groundwater
1Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India.
Scientific Reports
|October 6, 2025
Summary
A new simulation-optimization framework efficiently designs cost-effective groundwater remediation schemes. It integrates advanced algorithms to minimize costs and optimize well placement for contaminated aquifers.
Area of Science:
- Environmental Engineering
- Computational Hydrogeology
- Optimization Techniques
Background:
- Groundwater contamination poses significant environmental and economic challenges.
- Existing remediation strategies often lack cost-effectiveness and optimal design.
- Simulation-Optimization (SO) frameworks offer a promising approach for efficient remediation planning.
Purpose of the Study:
- To develop a robust SO framework for cost-effective groundwater remediation design.
- To integrate the Meshless Local Petrov Galerkin (MLPG) method with metaheuristic algorithms for enhanced simulation.
- To optimize extraction rates and well locations in Pump and Treat (PAT) schemes.
Main Methods:
- Coupled groundwater flow and transport simulation using the MLPG method.
- Integration of MLPG with Whale Optimization Algorithm (WOA), Aquila Optimization (AO), Golden Jackal Optimization (GJO), and Differential Evolution (DE).
- Application of the developed SO models (MLPG-WOA, MLPG-AO, MLPG-GJO, MLPG-DE) to hypothetical and field-type aquifer case studies.
Main Results:
- All SO models successfully designed remediation strategies within permissible contamination limits.
- MLPG-WOA identified a cost-effective single-well solution (INR 4,115,238) for the hypothetical aquifer.
- MLPG-DE determined an optimal nine-well strategy (INR 145,072,081) for the field-type aquifer.
- Optimal well placement zones were identified in both case studies.
Conclusions:
- The proposed SO framework provides efficient and reliable groundwater remediation designs.
- The MLPG-based SO models offer advantages such as minimal sensitivity to initial estimates and rapid convergence.
- These models can serve as effective alternatives to existing PAT remediation models.
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