Ant colony optimization and Bayesian analysis for long-term groundwater monitoring

This dissertation presents the work of groundwater long-term monitoring optimization based on an ant colony optimization algorithm and Bayesian analysis. Groundwater long-term monitoring (LTM) is required to assess human health and environmental risk of residual contaminants after active groundwater remediation activities are completed. However, LTM can be costly because of the large number of sampling locations and frequencies that exist at a site from previous site characterization and remediation activities.; Two LTM spatial sampling optimization methods based on ant colony optimization (ACO) algorithm were developed to identify optimal sampling networks that minimize the cost of LTM by reducing the number of monitoring locations with minimum overall data loss. The first method is called the primal ACO-LTM algorithm, which minimizes the number of remaining wells given the constraint on data loss quality, and it was implemented by binary decision variables. The second method is inspired by primal algorithm, and named as the dual ACO-LTM algorithm, here the role of the number of remaining wells is reversed from objective function to constraint, and this algorithm was to minimize the data loss given a fixed number of remaining wells. This dual ACO-LTM algorithm has a close analogy to the ACO paradigm for solving the traveling salesman problem (TSP). However, unlike the TSP problem, in the LTM problem, the ants will not necessarily visit all the wells. The ant terminates traveling when it has visited a given number of wells equal to the described number of redundant monitoring wells. Comparisons among the primal and dual ACO-LTM, the GA, and complete enumeration show that The dual ACO-LTM algorithm showed the best performance and identified global optimal solutions.; A statistical guideline for LTM temporal redundancy problem was proposed. Instead of relying on pollutant transport simulation models, this method is a data driven analysis approach. This study uses a Bayesian statistics-based methodology to optimize the scheduling of groundwater long-term monitoring. The technique combines information from different sets of observations over multiple sampling periods with spatial sampling optimization by ant colony optimization algorithm to provide probability distribution for future sampling schedule. Thus, the output of this method is not binary results (0/1), but fuzzy probabilistic scale (0∼1) for future monitoring schedule of each individual monitoring well. The results from medium size site were compared with those from other LTM design methods, including MAROS, CES, and 3-tiered approach. Similar but outperforming results with other methods verified that this method is a promising approach for LTM temporal problem.