Optimal Groundwater Sampling Network Design through Ant Colony Optimization

Groundwater long-term monitoring (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. The large number of sampling locations, number of constituents to be monitored, and the frequency of the sampling make the LTM costly, especially since LTM may be required over several decades. An optimization algorithm based on the ant colony optimization (ACO) paradigm for solving the traveling salesman problem (TSP) is proposed to reduce the number of monitoring wells while minimizing the overall data loss due to fewer 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. Ants depositing pheromones along their paths act as a form of indirect communication. The developed ACO-LTM algorithm is applied to a field site with an existing 30well LTM network. Optimal LTM networks with 27 to 21 wells, which represent a 10% to 30% reduction in sampling locations, resulted in overall data losses ranging from 0.383 to 1.74. Results from developed ACO-LTM algorithm provide a proof-of-concept for the application of the general ACO analogy to the groundwater LTM sampling location optimization problem.