Integrated fuzzy-stochastic modeling of petroleum contamination in subsurface

An integrated approach associated with fuzzy set theory, Monte Carlo simulation, and interval analysis was proposed to address the uncertainties in simulating petroleum contamination in the subsurface. The uncertainties in soil porosity and permeability had significant influences on the predicted benzene concentrations, while those of dispersivity had less impact. The results obtained from the proposed approach presented as probability distributions, membership functions, intervals, means, standard deviations, percentiles, and significance levels. They provided useful information regarding both system uncertainties and reliabilities under a combination of information with various data-quality levels.