Multiobjective Monitoring Network Design for Efficient Identification of Unknown Groundwater Pollution Sources Incorporating Genetic Programming–Based Monitoring

Abstract

In this study, a multiobjective model for optimal monitoring network design is presented, which aims to improve the accuracy of groundwater pollution source identification using concentration measurement from a designed optimal monitoring network. 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. A selected subset of trained GP models is used to compute the impact factor of pollutant source fluxes at candidate monitoring locations. This impact factor is used as design criteria to find the best monitoring locations. While constraining the maximum number of permissible monitoring locations, the designed monitoring network improves the results of source identification by choosing monitoring locations that reduce the possibility of missing a pollution source. At the same time, the designed monitoring network decreases the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the improved efficiency in source identification when concentration measurements from the designed monitoring network are used.