Development of Ant Colony Optimization for Long-Term Groundwater Monitoring

Abstract

Groundwater remediation projects require long-term monitoring (LTM) to assess compliance of active remedial systems and post-closure sites where groundwater contamination is still present. LTM can be costly given the large number of sampling locations, frequency of monitoring, and number of constituents monitored at a given site. This work presents the development of a methodology to optimize a groundwater-monitoring network in order to maximize cost-effectiveness without compromising program and data quality. We propose method that combines ant colony optimization (ACO) with a genetic algorithm (GA). The ACO method is inspired by the fact that ants are able to find the shortest route between their nest and a food source. This is accomplished by using pheromone trails as a form of indirect communication. Ant colony simulation techniques are adapted to minimize the number of monitoring locations in the sampling network without significant loss of information.