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Water Resources Management

, Volume 28, Issue 7, pp 2005–2019 | Cite as

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

  • J SreekanthEmail author
  • Bithin Datta
Article

Abstract

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.

Keywords

Saltwater intrusion Groundwater quality management Simulation-optimization Coastal aquifers 

Notes

Acknowledgments

This research was funded by Co-operative Research Centre for Contamination Assessment and Remediation of the Environment and partial support was obtained from James Cook University as tuition waiver for the first author.

References

  1. Aly AH, Peralta RC (1999) Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm. Water Resour Res 35(8):2523–2532CrossRefGoogle Scholar
  2. Ayvaz MT, Karahan H (2008) A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates. J Hydrol 357(1–2):76–92CrossRefGoogle Scholar
  3. Azamathulla HM, Ghani AA (2011) Genetic programming for predicting longitudinal dispersion coefficients in streams. Water Resour Manag 25(6):1537–1544CrossRefGoogle Scholar
  4. Bayer P, Bürger CM, Finkel M (2008) Computationally efficient stochastic optimization using multiple realizations. Adv Water Resour 31(2):399–417Google Scholar
  5. Bhattacharjya R, Datta B (2005) Optimal management of coastal aquifers using linked simulation optimization approach. Water Resour Manag 19(3):295–320CrossRefGoogle Scholar
  6. Chan N (1993) Robustness of the multiple realization method for stochastic hydraulic aquifer management. Water Resour Res 299:3159–3167Google Scholar
  7. Cheng AHD, Halhal D, Naji A, Ouazar D (2000) Pumping optimization in saltwater-intruded coastal aquifers. Water Resour Res 36(8):2155–2165CrossRefGoogle Scholar
  8. Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014) Estimation of monthly mean reference evapotranspiration in Turkey. Water Resour Manag 28(1):99–113CrossRefGoogle Scholar
  9. Das A, Datta B (1999a) Development of management models for sustainable use of coastal aquifers. J Irrig Drain Eng-Asce 125(3):112–121CrossRefGoogle Scholar
  10. Das A, Datta B (1999b) Development of multiobjective management models for coastal aquifers. J Water Resour Plan Manag-Asce 125(2):76–87CrossRefGoogle Scholar
  11. Datta B, Vennalakanti H, Dhar A (2009) Modeling and control of saltwater intrusion in a coastal aquifer of Andhra Pradesh, India. J Hydro Environ Res 3(3):148–159CrossRefGoogle Scholar
  12. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, HobokenGoogle Scholar
  13. Dhar A, Datta B (2009) Saltwater intrusion management of coastal aquifers. I: linked simulation-optimization. J Hydrol Eng 14(12):1263–1272CrossRefGoogle Scholar
  14. Fallah-Mehdipour E, Haddad OB, Mariño MA (2012) Real-time operation of reservoir system by genetic programming. Water Resour Manag 26(14):4091–4103CrossRefGoogle Scholar
  15. Feyen L, Gorelick SM (2004) Reliable groundwater management in hydroecologically sensitive areas. Water Resour Res 40(7)Google Scholar
  16. Feyen L, Gorelick SM (2005) Framework to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas. Water Resour Res 41(3)Google Scholar
  17. Katsifarakis KL, Petala Z (2006) Combining genetic algorithms and boundary elements to optimize coastal aquifers’ management. J Hydrol 327(1–2):200–207CrossRefGoogle Scholar
  18. Kourakos G, Mantoglou A (2009) Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Adv Water Resour 32(4):507–521CrossRefGoogle Scholar
  19. Koza JR (1994) Genetic programming as a means for programming computers by natural-selection. Stat Comput 4(2):87–112CrossRefGoogle Scholar
  20. Mantoglou A (2003) Pumping management of coastal aquifers using analytical models of saltwater intrusion. Water Resour Res 39(12). doi: 10.1029/2002WR001891
  21. Mantoglou A, Papantoniou M, Giannoulopoulos P (2004) Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms. J Hydrol 297(1–4):209–228CrossRefGoogle Scholar
  22. McMahon GA, Arunakumaren NJ, Bajracharya K (2000) Hydrogeological conceptualization of the Burdekin River Delta. In: Hydro 2000 – Proceedings f the 3rd international Hydrology and Water Resources Symposium of the Institution of Engineers, Perth, Western Australia, AustraliaGoogle Scholar
  23. Morgan DR, Eheart JW, Valocchi AJ (1993) Aquifer remediation design under uncertainty using a new chance constrained programming technique. Water Resour Res 29(3):551–561Google Scholar
  24. Narayan K, Schleeberger C, Bristow KL (2007) Modeling seawater intrusion in the Burdekin Delta Irrigation Area, North Queensland, Australia. J Agric Water Manag 89:217–228CrossRefGoogle Scholar
  25. Parasuraman K, Elshorbagy A (2008) Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resour Res 44(12). doi: 10.1029/2007WR006451
  26. Park CH, Aral MM (2004) Multi-objective optimization of pumping rates and well placement in coastal aquifers. J Hydrol 290(1–2):80–99CrossRefGoogle Scholar
  27. Ranjithan S, Eheart JW, Garrett JH (1993) Neural network based screening for groundwater reclamation under uncertainty. Water Resour Res 29(3):563–574CrossRefGoogle Scholar
  28. Sedki A, Ouazar D (2011) Simulation-optimization modeling for sustainable groundwater development: a Moroccan coastal aquifer case study. Water Resour Manag 25(11):2855–2875CrossRefGoogle Scholar
  29. Sreekanth J, Datta B (2010) Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. J Hydrol. doi: 10.1016/j.jhydrol.2010.08.023 Google Scholar
  30. Sreekanth J, Datta B (2011a) Coupled simulation-optimization model for coastal aquifer management using genetic programming based ensemble surrogate models and multiple-realization optimization. Water Resour Res 47, W04516. doi: 10.1029/2010WR009683 CrossRefGoogle Scholar
  31. Sreekanth J, Datta B (2011b) Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management. Water Resour Manag 25(13):3201–3218CrossRefGoogle Scholar
  32. Wagner BJ, Gorelick SM (1987) Optimal groundwater quality management under parameter uncertainty. Water Resour Res 23(7):1162–1174Google Scholar
  33. Wagner BJ, 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
  34. Yan SQ, Minsker B (2006) Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm. Water Resour Res 42(5):W05407CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Discipline of Civil and Environmental Engineering, School of Engineering and Physical SciencesJames Cook UniversityTownsvilleAustralia
  2. 2.CRC for Contaminant Assessment and Remediation of the EnvironmentMawson LakesAustralia
  3. 3.CSIRO Land and Water, Ecosciences PrecinctDutton ParkAustralia

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