Identification of contaminant source location and release history in aquifers

In this study, we formulate a contaminant source characterization problem as a nonlinear optimization model, in which contaminant source locations and release histories are defined as explicit unknown variables. The optimization model selected is the standard model, in which the residuals between the simulated and measured contaminant concentrations at observation sites are minimized. In the proposed formulation, simulated concentrations at the observation locations are implicitly embedded into the optimization model through the solution of ground-water flow and contaminant fate and transport simulation models. It is well known that repeated solutions of these models, which is a necessary component of the optimization process, dominate the computational cost and adversely affect the efficiency of this approach. To simplify this computationally intensive process, a new combinatorial approach, identified as the progressive genetic algorithm, is proposed for the solution of the nonlinear optimization model. Numerical experiments show that the proposed approach provides a robust tool for the solution of ground-water contaminant source identification problems.