Evolutionary Algorithms-Based Grid Computational Framework for Solving Groundwater Characterization Problems

This paper investigates groundwater system characterization problem, in this inverse problem the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optimization approach is a technique utilized to solve inverse problems by formulating them as an optimization model, where evolutionary algorithms are used to perform the search. In this approach, the PDE groundwater transport simulation model (simulation model) is solved iteratively during the evolutionary search, which in general can be computationally expensive. To overcome this limitation, high performance computing and grid computing are used to improve the simulation model efficiency. The parallel PDE groundwater transport simulation model is then coupled with an evolutionary computation search procedure to solve two instances of groundwater inverse problems. The results demonstrate the performance of the grid-enabled simulation-optimization approach in terms of solution quality and computational performance.