Water Resources Management

, Volume 21, Issue 5, pp 835–847 | Cite as

Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization



A methodology is developed for optimal remediation of groundwater aquifers under hydraulic conductivity uncertainty. A multi-objective management method based on a pump-and-treat remediation technology, is proposed. The pumping rates and well locations are the decision variables and two objectives are chosen: minimization of contaminated groundwater in the aquifer and minimization of remediation cost. A Monte Carlo simulation method is used to cope with hydraulic conductivity uncertainty. A number of equally probable realizations of hydraulic conductivity are created and a Pareto front is obtained using a modified multi-objective Genetic Algorithm. A penalty function is utilized to maintain the algebraic sum of pumping and recharging rates equal to zero. Since Monte Carlo simulations are CPU time consuming, a method is proposed to identify the few significant realizations which have an effect on the optimal solution (critical realizations). A Pareto front with an assigned probability is derived, so that the decision maker can make decisions with specified reliability. In a case study with 100 realizations, only 11 realizations were found critical and need be considered. The remaining 89 realizations consistently obtain low ranks for all designs considered and do not affect decisions at 95% reliability level. Thus these realizations need not be considered which implies a 89% savings in computer time. The designs obtained using the critical realizations, retain a similar reliability for new realizations not considered in the design process.

Key words

groundwater remediation multi-objective optimization Monte Carlo simulation uncertain hydraulic conductivity critical realizations 


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  1. Andricevic R, Kitanidis PK (1990) Oprimization of the pumping schedule in aquifer remediation under uncertainty. Water Resour Res 26(5):875–885CrossRefGoogle Scholar
  2. Erickson M, Mayer A, Horn J (2002) Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Adv Water Resour 25:51–65CrossRefGoogle Scholar
  3. Espinoza FP, Minsker BS, Golberg DE (2005) Adaptive hybrid genetic algorithm for groundwater remediation design. J Water Resour Plan Manage 131(1):14–24CrossRefGoogle Scholar
  4. Gen M, Cheng R (2000) Genetic algorithms and engineering optimization, 495. Wiley, New YorkGoogle Scholar
  5. Guan J, Aral MM (1999) Optimal remediation with well locations and pumping rates selected as continuous decision variables. J Hydrol 221:20–42CrossRefGoogle Scholar
  6. Mantoglou A, Wilson JL (1982) The turning bands method for simulation of random fields using line generation by a spectral method. Water Resour Res 18(5):1379–1397Google Scholar
  7. Maskey S, Jonoski A, Solomatine DP (2002) Groundwater remediation strategy using global optimization algorithms. J Water Resour Plan Manage 128(6):431–440CrossRefGoogle Scholar
  8. McDonald GM, Harbaugh WA (1988) A modular three-dimensional finite-difference ground-water flow model. Techniques of water resources investigations 06-A1. United States Geological Survey, Washington, DCGoogle Scholar
  9. McKinney DC, Lin M-D (1996) Pump-and-treat ground-water remediation system optimization. J Water Resour Plan Manage 122(2):128–136CrossRefGoogle Scholar
  10. Park C-H, Aral MM (2004) Multi-objective optimization of pumping rates and well placement in coastal aquifers. J Hydrol 290:80–99CrossRefGoogle Scholar
  11. Ranjithan S, Eheart JW, Garrett HJ (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour Res 29(3):563–574CrossRefGoogle Scholar
  12. Singh A, Minsker B (2003) Uncertainty based multi-objective optimization of groundwater remediation at umatilla chemical depot. In: Proceedings of the American Society of Civil Engineers (ASCE) Environmental and Water Resources Institute (EWRI) World Water and Environmental Resources Congress 2003, Philadelphia PAGoogle Scholar
  13. Singh A, Minsker B, Goldberg DE (2003) Combining reliability and pareto optimality – an approach using stochastic multi-objective genetic algorithms. In: Proceedings of the American Society of Civil Engineers (ASCE) Environmental and Water Resources Institute (EWRI) World Water and Environmental Resources Congress 2003, Philadelphia PAGoogle Scholar
  14. Wagner JB, Gorelick SM (1989) Reliable aquifer remediation in the presence of spatially variable hydraulic conductivity: from data to design. Water Resour Res 25(10):2211–2225Google Scholar
  15. Wong HS, Yeh WW-G (2002) Uncertainty analysis in contaminated aquifer management. J Water Resour Plan Manage 128(1):33–45CrossRefGoogle Scholar
  16. Zheng C, Wang PP (1999) MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion and chemical reactions of contaminants in ground water systems: documentation and user’s guide. Contact Report SERDP-99-1, US Army Engineer Research and Development Center, Vicksburg, MSGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Rural and Surveying EngineeringNational Technical University of AthensAthensGreece

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