Optimal groundwater monitoring design using an ant colony optimization paradigm

Abstract

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 can make the LTM costly, especially since LTM may be required over several decades. An optimization algorithm based on the ant colony optimization (ACO) paradigm is developed to minimize the overall data loss due to fewer sampling locations for a given number of monitoring wells. The ACO method is inspired by the ability of an ant colony to identify the shortest route between their nest and a food source. The developed ACO-LTM algorithm is applied to a field site with an existing 30-well LTM network. When compared to the results identified through complete enumeration, the ACO-LTM solutions are globally optimal for the cases with 21 to 27 remaining wells. Results from the 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. A major contribution of this work is the successful development of an efficient and effective stochastic search algorithm for solving the LTM optimization problem based on the ACO paradigm.