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Remediation of Heterogeneous Aquifers Subject to Uncertainty
Author(s) -
Ricciardi K. L.
Publication year - 2009
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2009.00581.x
Subject(s) - environmental remediation , groundwater remediation , computer science , uncertainty analysis , aquifer , constraint (computer aided design) , uncertainty quantification , orientation (vector space) , mathematical optimization , optimal design , reliability (semiconductor) , groundwater , environmental science , reliability engineering , machine learning , engineering , simulation , mathematics , geotechnical engineering , contamination , mechanical engineering , ecology , power (physics) , physics , geometry , quantum mechanics , biology
Optimal cost pump‐and‐treat ground water remediation designs for containment of a contaminated aquifer are often developed using deterministic ground water models to predict ground water flow. Uncertainty in hydraulic conductivity fields used in these models results in remediation designs that are unreliable. The degree to which uncertainty contributes to the reliability of remediation designs as measured by the characterization of the uncertainty is shown to differ depending upon the geologic environments of the models. This conclusion is drawn from the optimal design costs for multiple deterministic models generated to represent the uncertainty of four distinct models with different geologic environments. A multi scenario approach that includes uncertainty into the remediation design called the deterministic method for optimization subject to uncertainty (DMOU) is applied to these distinct models. It is found that the DMOU is a method for determining a remediation design subject to uncertainty that requires minimal postprocessing efforts. Preprocessing, however, is required for the application of the DMOU to unique problems. In the ground water remediation design problems, the orientation of geologic facies with respect to the orientation of flow patterns, pumping well locations, and constraint locations are shown to affect the preprocessing, the solutions to the DMOU problems, and the computational efficiency of the DMOU approach. The results of the DMOU are compared to the results of a statistical analysis of the effects of the uncertainty on remediation designs. This comparison validates the efficacy of the DMOU and illustrates the computational advantages to using the DMOU over statistical measures.