Abstract
A methodology for optimizing groundwater long-term monitoring (LTM) is presented. Groundwater LTM is required to assess the performance of groundwater remediation and human being health risk at post-closure sites where groundwater contaminants are still present. Some monitoring wells in the existing LTM network may be redundant, making it possible to remove some of them without compromising data quality. An optimization algorithm based on the ant colony optimization (ACO) paradigm is developed to minimize the overall data loss by identifying a given number redundant sampling locations. The ACO method is inspired by the ability of ant colony to identify the shortest route between their nest and a food source. The algorithm searches for redundant wells from among sampling locations in the monitoring network and follows steps analogous to traveling salesman problem (TSP), which is a cardinal combinatorial problem successfully solved by ACO. Results from the developed ACO-LTM algorithm show global optima or near-optimal solutions were identified. A comparison of the ACOLTM results to those from complete enumeration indicates that the developed ACO-LTM algorithm is efficient and effective.
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