Pollution source identification in groundwater systems: Application of regret theory and Bayesian networks

Seyyed Nasser Bashi-Azghadi, Reza Kerachian*, Mohammad Reza Bazargan-Lari, Mohammad Reza Nikoo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Pollution source identification in groundwater resources is a challenging task due to existing uncertainties in both pollutant source characteristics and mass transport in porous media. To obtain a robust and cost-effective groundwater monitoring configuration, this paper presents a new regret-based optimization model which minimizes the number of monitoring wells and average regret in estimating undetected polluted area. A Monte Carlo analysis is used to consider existing uncertainties in both pollution source characteristics and parameters of groundwater quality simulation model. MODFLOW and MT3D, groundwater quantity and quality simulation models, are used to simulate the spatial and temporal variations of a water quality indicator in groundwater. For each non-dominated solution provided by a bi-objective optimization model, the optimal positions of monitoring wells are also determined by minimizing the corresponding regret in undetected polluted area. Furthermore, a Bayesian network (BN) is trained and validated based on results of the Monte Carlo analysis. The trained BN is capable of accurately determining an unknown pollution source using monitoring data in real-time operation of monitoring system. To demonstrate the efficiency and applicability of the proposed methodology, it is applied to the Tehran Aquifer in the Tehran refinery region, which is highly polluted due to leakage from several tanks of petroleum products.

Original languageEnglish
Pages (from-to)241-249
Number of pages9
JournalIranian Journal of Science and Technology - Transactions of Civil Engineering
Issue number3
Publication statusPublished - Sept 2016


  • Bayesian networks (BNs)
  • Groundwater monitoring
  • Pollution source identification
  • Regret theory
  • Tehran refinery

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology


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