Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers

The exact location and release history of groundwater pollutant sources is often unknown. Identification of unknown release histories is usually carried out by inversion of the equations governing flow and transport over time and space. This is an ill posed problem. Solution of this ill-posed inversion is complicated due to the inherent non-uniqueness of solutions, uncertainties in modelling the flow and transport processes in the aquifer and unavoidable concentration measurement errors. Several methods to solve the ill posed inversion problem have been suggested in past. The simulation-optimization approach using global heuristic search optimization methods has been found to be the most effective with regards to accuracy of solutions. However, these methods are computationally intensive. A linked simulation-optimization based methodology using a variant of simulated annealing (SA) algorithm is linked to the numerical models used to simulate flow (MODFLOW) and transport processes (MT3DMS). The objective f...

[1]  S. Gorelick,et al.  Identifying sources of groundwater pollution: An optimization approach , 1983 .

[2]  Mustafa M. Aral,et al.  Identification of Contaminant Source Location and Release History in Aquifers , 2001 .

[3]  Bithin Datta,et al.  Development of an expert-system embedding pattern-recognition techniques for pollution-source identification. Report for 30 September 1987-29 November 1989 , 1989 .

[4]  P. Kitanidis,et al.  A geostatistical approach to contaminant source identification , 1997 .

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Bithin Datta,et al.  Optimal Identification of Ground-Water Pollution Sources and Parameter Estimation , 2001 .

[7]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[8]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[9]  Arlen W. Harbaugh,et al.  A generalized finite-difference formulation for the U.S. Geological Survey modular three-dimensional finite-difference ground-water flow model , 1992 .

[10]  Bithin Datta,et al.  Identification of Pollution Sources in Transient Groundwater Systems , 2000 .

[11]  A. Bagtzoglou,et al.  State of the Art Report on Mathematical Methods for Groundwater Pollution Source Identification , 2001 .

[12]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[13]  Lester Ingber,et al.  Adaptive simulated annealing (ASA): Lessons learned , 2000, ArXiv.

[14]  Iraj Javandel,et al.  Groundwater Transport: Handbook of Mathematical Models , 1984 .

[15]  I ScottKirkpatrick Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[16]  B. Datta,et al.  Identification of groundwater pollution sources using GA-based linked simulation optimization model , 2006 .

[17]  Chunmiao Zheng,et al.  A Field Demonstration of the Simulation Optimization Approach for Remediation System Design , 2002, Ground water.

[18]  Ashu Jain,et al.  Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks , 2004 .

[19]  N. Sun Inverse problems in groundwater modeling , 1994 .

[20]  Bithin Datta,et al.  Optimal Monitoring Network and Ground-Water–Pollution Source Identification , 1997 .

[21]  Chris Reddy Introduction to Guest Editorial , 2002 .

[22]  G. Mahinthakumar,et al.  Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems , 2005 .

[23]  C. Zheng A Modular Three-Dimensional Transport Model for Simulation of Advection, Dispersion and Chemical Reaction of Contaminants in Groundwater Systems , 1990 .