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Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization

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Abstract

In this study, a simulation-optimization (SO) model is presented by coupling a meshfree based simulator using radial point collocation method (RPCM) and particle swarm optimization (PSO) as optimizer to identify the unknown groundwater contaminant sources from the measured/simulated contaminant concentration data in the aquifer. To demonstrate the approach, two case studies have been presented. The first example is a hypothetical case which simulates the contaminant releases from several disposal sites in an aquifer during four years release period. The second case considered is a field study where leaching of contaminant, during their storage, from disposal sites at several locations in the aquifer leads to contamination of the groundwater. The goal in both cases was to reconstruct the contaminant release history from the disposal sites and their magnitudes from the given historical concentration data at a few observation wells in the aquifer. It was observed that the source identification model could reconstruct the release histories from the waste disposal sites in both the cases accurately. This study demonstrated that PSO based optimization model with a meshfree flow and transport simulator can be effectively used for groundwater contaminant source identification problems.

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Authors and Affiliations

  1. Health Physics Division, Bhabha Atomic Research Centre, Trombay, Mumbai, India

    L. Guneshwor

  2. Civil Engineering Department, Indian Institute of Technology Bombay, Powai, Mumbai, India

    T. I. Eldho

  3. Radiation Safety Systems Divisions, Bhabha Atomic Research Centre, Trombay, Mumbai, India

    A. Vinod Kumar

Authors
  1. L. Guneshwor
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  2. T. I. Eldho
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  3. A. Vinod Kumar
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Correspondence to T. I. Eldho.

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Guneshwor, L., Eldho, T.I. & Vinod Kumar, A. Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization. Water Resour Manage 32, 1517–1538 (2018). https://doi.org/10.1007/s11269-017-1885-1

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  • DOI: https://doi.org/10.1007/s11269-017-1885-1

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