Use of Optimization Methods in Identification of

Release History and Contaminant Source Locations

Dr. Mustafa M. Aral and Dr. Jiabao Guan
Multimedia Environmental Simulations Laboratory
School of Civil and Environmental Engineering
Georgia Institute of Technology
Atlanta, Georgia 30332

Back to MESL Research
Click here to start slide presentation


Recovering the release histories and identifying the locations of contaminant sources in heterogeneous aquifers are an important and also a challenging problem. In this study, the source identification problem was formulated as a nonlinear optimization model, in which contaminant source locations and release histories were defined as explicit unknown variables. The optimization model selected is the standard model, where residuals between the simulated and measured contaminant concentrations at observation sites are minimized. In this formulation, simulated concentrations at the observation locations are implicitly embedded into the optimization model through the solution of groundwater flow and contaminant fate and transport simulation models. It is well known that repeated solution of these models, which is a necessary component of the optimization process, dominates the computational cost and adversely affects the efficiency of this approach. To simplify this computationally intensive process, a new combinatorial approach, identified as Progressive Genetic Algorithm (PGA), is proposed for the solution of the nonlinear optimization model. PGA is a subdomain method, which links the standard genetic algorithm (GA) with groundwater simulation models within a subdomain. In this approach the optimization process is divided into iteration and search stages. During the iteration stage, groundwater simulation models are run to generate a subdomain optimization model, and in the search stage, GA is used to search for the optimal solution in the subdomain. During this iterative cycle, the position and the size of the subdomain continuously changes, yielding an efficient solution process. Numerical results in a randomly heterogeneous aquifer show that the proposed approach provides a robust tool for the solution of groundwater contaminant source identification problem. The proposed approach may also be used in the solution of other nonlinear optimization problems, which are computationally intensive. Click here to start slide presentation

Table of Contents

Back to MESL Home Page