Abstract
Surveillance system for an aquatic environment is an important aspect of water quality monitoring in lakes and oceans. It is well known that long-term surveillance of aquatic environments is a costly endeavor, thus a sound strategy is necessary to select the best locations of monitoring stations to improve the performance of a monitoring system. This can be accomplished by optimizing the locations of a sensor network with respect to the hydraulic and fate and transport characteristics of the surface water system. It is expected that such an approach will improve the effectiveness of the monitoring system and also reduce the overall cost. Since the hydrodynamics and the contaminant migration pathways in a surface water environment are complex, the optimal solution to this problem is complex. To solve this problem in a simplified form, a 2-dimensional hydrodynamic simulation model is developed using a finite element method. The best sensor locations are selected to minimize detection time of the contaminant presence. Due to nonlinear nature of the model Genetic algorithm (GA) is used in optimization formulation to select the best sensor network. Furthermore, this computation processes require a significant amount of computational time, thus parallel computing is adopted to reduce computation time.
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