Saltwater Intrusion Management of Coastal Aquifers. II: Operation Uncertainty and Monitoring
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 12
Abstract
Management of saltwater intrusion in coastal aquifers should be based on robust management strategies and monitoring of their impacts. A robust optimal management strategy is less sensitive to deviations from prescribed strategies at the field level. Development of robust management framework is an important issue that needs attention especially when it results in near optimal strategies even when deviations from prescribed strategies occur in the field implementation stage. Implementation of a strategy requires field scale monitoring to determine the impact in terms of compliance with management goals due to possible deviation from an optimal prescribed strategy. Design of such an optimal monitoring network for compliance also requires robust optimal design due to the uncertainties involved. Deviations from prescribed strategies in the field are often more sensitive to uncertainties in the implementation phase. A multiple objective management model for robust optimal management of saltwater intrusion in coastal aquifers is proposed. Both risk neutral and risk-based management model formulations are presented. A robust monitoring network design methodology is also proposed for compliance monitoring of proposed robust management strategies. Performances of the developed methodology are tested for an illustrative coastal aquifer study area, as presented by Dhar and Datta. Performance evaluations show potential applicability of the developed methodologies and some of the relative advantages.
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References
Aly, A. H., and Peralta, R. C. (1999). “Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm.” Water Resour. Res., 35(8), 2523–2532.
Branke, J. (2002). Evolutionary optimization in dynamic environments, Kluwer Academic, Boston.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, New York.
Deb, K., and Gupta, H. (2004). “Introducing robustness in multi-objective optimization.” KanGAL Rep. No. 2004016, Indian Institute of Technology Kanpur, Kanpur, India.
Deb, K., and Gupta, H. (2005). “A constraint handling strategy for robust multi-criterion optimization.” KanGAL Rep. No. 2005001, Indian Institute of Technology Kanpur, Kanpur, India.
Dhar, A. (2007). “Optimal management and monitoring of coastal aquifers and other contaminated aquifers.” Ph.D. thesis, Indian Institute of Technology Kanpur, Kanpur, India.
Dhar, A., and Datta, B. (2009). “Saltwater intrusion management of coastal aquifers. I: Linked simulation-optimization.” J. Hydrol. Eng., 14.
Freeze, R. A., and Gorelick, S. M. (1999). “Convergence of stochastic optimization and decision analysis in engineering design of aquifer remediation.” Ground Water, 37(6), 934–954.
Gunawan, S., and Azarm, S. (2005). “Multi-objective robust optimization using a sensitivity region concept.” Struct. Multidiscip. Optim., 29(1), 50–60.
ILOG CPLEX 7.0 user’s manual. (2000). ILOG SA.
Jin, Y., and Sendhoff, B. (2003). “Trade-off between performance and robustness: An evolutionary multiobjective approach.” Lect. Notes Comput. Sci., 2632, 237–251.
Kalwij, I. M., and Peralta, R. C. (2006). “Simulation/optimization modeling for robust pumping strategy design.” Ground Water, 44(4), 574–582.
Loucks, D. P., and van Beek, E. (2005). Water resources systems planning and management: An introduction to methods, models and applications, UNESCO Publishing, Delft Hydraulics, The Netherlands.
McKay, M. D., Conover, W. J., and Beckman, R. J. (1979). “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code.” Technometrics, 21, 239–245.
Olvander, J. (2005). “Robustness considerations in multi-objective optimal design.” J. Eng. Design, 16(5), 511–523.
Paenke, I., Branke, J., and Jin, Y. (2006). “Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation.” IEEE Trans. Evol. Comput., 10(4), 405–420.
Ray, T. (2002). “Constrained robust optimal design using a multiobjective evolutionary algorithm.” IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computating (CEC 2002), Hawaii, 419–424.
Ricciardi, K. L., Pinder, G. F., and Karatzas, G. P. (2007). “Efficient groundwater remediation system design subject to uncertainty using robust optimization.” J. Water Resour. Plann. Manage., 133(3), 253–263.
Shimoyama, K., Oyama, A., and Fujii, K. (2005). “A new efficient and useful robust optimization approach-design for multi-objective six sigma.” Proc., 2005 IEEE Congress on Evolutionary Computation (CEC'2005), Vol. 1, Edinburgh, Scotland, 950–957.
Smalley, J. B., Minsker, B. S., and Goldberg, D. E. (2000). “Risk based in situ bioremediation design using a noisy genetic algorithm.” Water Resour. Res., 36(10), 3043–3052.
Wagner, B. J. (1999). “Evaluating data worth for ground-water management under uncertainty.” J. Water Resour. Plann. Manage., 125(5), 281–288.
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Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 12 • December 2009
Pages: 1273 - 1282
Copyright
© 2009 ASCE.
History
Received: Mar 1, 2008
Accepted: May 29, 2009
Published online: Nov 13, 2009
Published in print: Dec 2009
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