Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.