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Aggregated GP-based Optimization for Contaminant Source Localization

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  • Krityakierne, Tipaluck
  • Baowan, Duangkamon

Abstract

Recently a new simulation-based optimization benchmark of groundwater contaminant source localization problems has been introduced to the hydrogeological science community. Given information on contaminant concentration levels at each monitoring well and each time step, its objective is to identify the location of contaminant source. In this work, we analyze and look at the problem from different angles to gain more insights on this class of groundwater problems. To tackle the problem, a novel simulation-based optimization algorithm relying on an aggregated Gaussian process model, and the expected improvement criterion is introduced. Results from this study show that the proposed algorithm, though relying on an approximated Gaussian process model, demonstrates superior efficiency and reliability than a traditional expected improvement-based algorithm. The location of the monitoring wells was confirmed to play a crucial role in assisting the optimization algorithm to accurately localize the contaminant source. Additional monitoring wells, while adding more knowledge of the space-time mapping of concentration levels, could nevertheless slow down convergence of the algorithm due to the increase in problem complexity.

Suggested Citation

  • Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
    • Handle: RePEc:eee:oprepe:v:7:y:2020:i:c:s2214716020300415
    DOI: 10.1016/j.orp.2020.100151
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    References listed on IDEAS

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