| The complexity of geologic and hydrologic subsurface structures often makes it difficult to formulate an accurate groundwater model. To improve the accuracy of a model, many studies have focused on optimizing unknown parameters without considering their uncertainty. The Information Theory (IT) approach uses entropy as a measure of uncertainty for the most probable state of a system. We maximize entropy when the groundwater model is optimized, by imposing normalization constraint and error constraint with observation data. A three-dimensional synthetic model is simulated using MODFLOW-2000 to demonstrate the effectiveness of the IT approach. With hydraulic heads as the observed data, hydraulic conductivities are chosen as the parameters to be optimized. The IT approach calculates variance, covariance and correlation coefficients for multiple unknown parameters. The Kansas City Plant case study shows that the IT approach is able to direct site exploration to detect the unknown structure that may affect the remediation performance. |
