Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison between simulated annealing and particle swarm optimization

Groundwater pollution is the biggest threat to sustainability of groundwater resources and even more difficult to detect in case of clandestine sources. At the time when pollution is first detected in randomly located sparse wells, very little is known about the pollution sources. Finding the precise locations of clandestine sources of pollution and their release flux history is the biggest challenge and often termed as a problem belonging to the class of environmental forensics. In this study, two linked simulation optimization–based novel techniques are developed to estimate locations and release flux history from clandestine point sources of groundwater pollution. Simulation model is clubbed with optimization solver to determine the locations and release flux histories of groundwater pollution sources by minimizing the residual error between observed and simulated concentration values. Simulated annealing (SA) and particle swarm optimization (PSO) are used as optimization algorithms. A detailed comparative analysis of these two meta-heuristic optimization algorithms in minimizing the residual error is presented in this study. The performance evaluation of both the algorithms in identifying the sources locations and release flux history is carried out for two synthetic cases and a real-life scenario of groundwater pollution in an aquifer in New South Wales, Australia, which has not been attempted in the past. The results of source location identification and release flux history show the selective applicability of each algorithm in solving real-life scenarios of groundwater pollution.