Characterizing Uncertainties Associated with Contaminant Transport Modeling through a Coupled Fuzzy-Stochastic Approach

A factorial-design-based fuzzy-stochastic modeling system (FFSMS) was developed in this study to systematically investigate impacts of uncertainties associated with hydrocarbon contaminant transport in subsurface through integration of a compositional model, factorial design method, fuzzy modeling approach and Monte Carlo simulation technique. The goodness of fit of the numerical model was analyzed by means of a pilot-scale experimental system. Once the model was calibrated, it was used in order to predict the contaminant concentration depending on values of several parameters including intrinsic permeability, porosity, and longitudinal dispersivity. These parameters were imprecisely known, and such an imprecision was handled by means of both fuzzy sets and/or stochastic theory. The individual and joint effects of these uncertain parameters were analyzed by modeling the dependence between the prediction and the imprecise parameters (factors) through factorial design analysis. The study results indicated that the uncertainties associated with input parameters had significant impacts on modeling outputs; the degree of influence of each model input varied significantly with the level of its imprecision. The study results demonstrated that proposed FFSMS can efficiently analyze the impact of different uncertainty sources associated with different hydrogeological parameters on the prediction of the hydrocarbon concentrations in groundwater. Such studies would provide strong basis for performing successful risk assessment and efficient remediation design for the management of contaminated site.