Optimization-based methodologies for detection and monitoring of groundwater sources

Finding the location and concentration of groundwater contaminant sources typically requires the solution of an inverse problem. A parallel hybrid genetic algorithm (GA) optimization framework that uses genetic algorithms (GA) coupled with local search approaches (GA-LS) has been developed previously to solve groundwater inverse problems. In this study, the identification of an emplaced source at the Borden site is carried out as a test problem using this optimization framework by using Real Genetic Algorithm (RGA) as the GA approach and Nelder-Mead simplex as the LS approach. The RGA results showed that the minimum objective function did not always correspond to the minimum solution error indicating a possible non-uniqueness issue. To address this problem, a procedure to identify maximally different starting points for LS is introduced. When measurement or model errors are non-existent it is shown that one of these starting points leads to the true solution. When these errors are significant, this procedure leads to multiple possible solutions that could be used as a basis for further investigation. Metrics of mean and standard deviation of objective function values were studied to evaluate the possible solutions. After comparison, the objective function is suggested to find the best alternative. This suggests that this alternative generation procedure could be used to address the non-uniqueness of inverse problems.;Another reason that can lead to non-uniqueness is the inappropriate placement of monitoring wells and sampling frequency of observation data. Studies show that source identification accuracy is dependent on the observation sampling location, i.e., different sampling locations will result in different performance in terms of objective function values and solution errors. So finding a set of optimal monitoring well locations is very important for characterizing the source. A sensitivity-based method was proposed for optimal placement of monitoring wells by incorporating two uncertainties: the source location and hydraulic conductivity. An optimality metric called D-optimality in combination with a distance metric, which tends to make monitoring locations as far apart from each other as possible, is developed for finding optimal monitoring well locations for source identification. To address uncertainty in hydraulic conductivity, an integration method of multiple well designs is proposed based on multiple hydraulic conductivity realizations. Genetic algorithm is used as a search technique for this discrete combinatorial optimization problem. This procedure was applied to a realistic problem based on the well-known Borden Site data in Canada. The results show that the criterion based selection proposed in this research provides improved source identification performance when compared to manually selected placement of wells.