Incorporating subjective and stochastic uncertainty in an interactive multi-objective groundwater calibration framework

Abhishek Singh, Douglas D. Walker, Barbara S. Minsker, Albert J. Valocchi

Research output: Contribution to journalArticlepeer-review


The interactive multi-objective genetic algorithm (IMOGA) combines traditional optimization with an interactive framework that considers the subjective knowledge of hydro-geological experts in addition to quantitative calibration measures such as calibration errors and regularization to solve the groundwater inverse problem. The IMOGA is inherently a deterministic framework and identifies multiple large-scale parameter fields (typically head and transmissivity data are used to identify transmissivity fields). These large-scale parameter fields represent the optimal trade-offs between the different criteria (quantitative and qualitative) used in the IMOGA. This paper further extends the IMOGA to incorporate uncertainty both in the large-scale trends as well as the small-scale variability (which can not be resolved using the field data) in the parameter fields. The different parameter fields identified by the IMOGA represent the uncertainty in large-scale trends, and this uncertainty is modeled using a Bayesian approach where calibration error, regularization, and the expert's subjective preference are combined to compute a likelihood metric for each parameter field. Small-scale (stochastic) variability is modeled using a geostatistical approach and added onto the large-scale trends identified by the IMOGA. This approach is applied to the Waste Isolation Pilot Plant (WIPP) case-study. Results, with and without expert interaction, are analyzed and the impact that expert judgment has on predictive uncertainty at the WIPP site is discussed. It is shown that for this case, expert interaction leads to more conservative solutions as the expert compensates for some of the lack of data and modeling approximations introduced in the formulation of the problem.

Original languageEnglish (US)
Pages (from-to)881-898
Number of pages18
JournalStochastic Environmental Research and Risk Assessment
Issue number6
StatePublished - 2010


  • Calibration
  • Genetic algorithm
  • Groundwater
  • Interactive
  • Multi-objective
  • Regularization
  • Stochastic
  • Uncertainty

ASJC Scopus subject areas

  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science
  • Environmental Engineering
  • Environmental Chemistry


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