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A Grid-Enabled Simulation-Optimization Approach For Solving Groundwater Characterization Problems

Groundwater environmental characterization involves the resolution of unknown system characteristics from observation data, and is classified as an inverse problem. Inverse problems are relatively challenging to solve due to natural ill-posedness and computational intractability. Here we adopt the use of a simulation-optimization approach that couples a numerical pollutant-transport simulation model with evolution strategies for solution of the inverse problem. In this approach, the numerical transport model is solved iteratively during the evolutionary search, which in general can be computationally intensive since several hundreds to thousands of forward model evaluations are typically required for solution. Given the potential computational intractability of such a simulation-optimization approach, grid computing is explored as a possibility to ease and enable the solution of such problems. In this paper, the solution of two groundwater inverse problems will be explored. The computational experiments were performed on the National Scientific Foundation TeraGrid. The results demonstrate the performance of the grid-enabled simulation-optimization approach in terms of solution quality and computational performance.