Title: Optimisation approach for pollution source identification in groundwater: an overview
Author: Sreenivasulu Chadalavada, Bithin Datta, Ravi Naidu
Cooperative Research Centre for Contamination Assessment and Remediation of the Environment, P.O. Box 486, Salisbury South, South Australia 5106, Australia.
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India; and Civil and Environmental Engineering, School of Engineering, James Cook University, Townsville, QLD 4811, Australia.
Centre for Environmental Risk Assessment and Remediation, University of South, Australia, Mawson Lakes, SA 5095, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment, P.O. Box 486, Salisbury South, South Australia 5106, Australia
Abstract: Groundwater pollution occurs from different anthropogenic sources like leakage from Underground Storage Tanks (USTs) and depositories, leakage from hazardous waste dump sites and soak pits. Remediation of these contaminated sites requires optimal decision-making system so that the remediation is done in a cost-effective and efficient manner. Identification of unknown pollution sources plays an important role in remediation and containment of contaminant plume in a hazardous site. This paper reviews different optimisation algorithms like classical, nonclassical such as Genetic Algorithm, Artificial Neural Network and Simulated Annealing and hybrid methods, which can be applied for optimal identification of unknown groundwater pollution sources.
Keywords: groundwater contamination; pollution sources; source identification; optimisation; monitoring networks; water pollution; genetic algorithms; artificial neural networks; ANNs; simulated annealing.
Int. J. of Environment and Waste Management, 2011 Vol.8, No.1/2, pp.40 - 61
Available online: 27 Jun 2011