Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison betwe
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(2020) 192:791
Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison between simulated annealing and particle swarm optimization Anirban Chakraborty
· Om Prakash
Received: 10 March 2020 / Accepted: 15 October 2020 © Springer Nature Switzerland AG 2020
Abstract 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 reallife scenario of groundwater pollution in an aquifer Anirban Chakraborty () · Om Prakash Civil and Environmental Engineering, Indian Institute of Technology Patna, Bihta, Bihar India e-mail: [email protected]
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. Keywords Groundwater pollution · Clandestine source identification · Linked simulation optimization · Simulated annealing · Particle swarm optimization
Introduction Groundwater plays an important role in meeting the demand for freshwater. Groundwater quality has degraded due to widespread pollution from anthropogenic activities. Groundwater sources are polluted mainly due to contaminants arising from increased industrial activity and the use of chemicals in agriculture (Fried 1975). The common causes of groundwater contamination include leakage of underground pipes or reservoirs, drainage lagoons, accidental leaks, landfill leachate, and improper disposal of chemical wastes. Deterioration of groundwater quality has given rise to a range of remediation techniques for reclaiming such polluted aquifers. A necessary step to ensure sustainability of these groundwater resources would be reclamation of polluted groundwater aquifers (Yan and Minsker 2006). Ef
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