Advertisement

Multi-objective Freshwater Management in Coastal Aquifers Under Uncertainty in Hydraulic Parameters

  • Ali Ranjbar
  • Najmeh MahjouriEmail author
Original Paper
  • 23 Downloads

Abstract

This paper proposes a novel stochastic framework for groundwater quantity and quality management in aquifers threatened by saltwater intrusion. In this methodology, a finite difference SEAWAT code is linked with an optimization model to solve density-dependent groundwater flow equations considering different patterns of pumping rates. To reduce the computational time of the simulation–optimization process especially when there are a high number of decision variables, a modular evolutionary polynomial regression (MEPR) model is developed and coupled with the optimization algorithm. The info-gap theory is utilized to evaluate the robustness of optimal scenarios incorporating the uncertainty of hydraulic conductivity (k) of the heterogeneous aquifer. For each management scenario proposed by the simulation–optimization model, values of robustness and opportuneness indices are computed based on utility functions of different agricultural sectors. The results of applying the proposed method to the Qom aquifer in Iran show that coupling MEPR model with the simulation–optimization model considering the uncertainty of the aquifer parameter k could provide a reliable management scenario with a comparatively low computational cost.

Keywords

Saltwater intrusion Info-gap theory Deep uncertainty Qom aquifer Evolutionary polynomial regression 

References

  1. Abarca, E., Vazquez-Sune, E., Carrera, J., Capino, B., Gámez, D., & Batlle, F. (2006). Optimal design of measures to correct seawater intrusion. Water Resources Research,42(9), W09415.CrossRefGoogle Scholar
  2. Abd-Elhamid, H. F., & Javadi, A. A. (2011). A cost-effective method to control seawater intrusion in coastal aquifers. Water Resources Management,25(11), 2755–2780.CrossRefGoogle Scholar
  3. Alizadeh, Z., & Mahjouri, N. (2017) A spatiotemporal Bayesian maximum entropy-based methodology for dealing with sparse data in revising groundwater quality monitoring networks: The Tehran region experience. Environmental Earth Sciences, 76(12), 436.CrossRefGoogle Scholar
  4. Ben-Haim, Y. (2001). Information-gap decision theory: Decisions under severe uncertainty. San Diego, CA: Academic Press.Google Scholar
  5. Bhattacharjya, R. K., & Datta, B. (2005). Optimal management of coastal aquifers using linked simulation optimization approach. Water Resources Management,19(3), 295–320.CrossRefGoogle Scholar
  6. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation,6(2), 182–197.CrossRefGoogle Scholar
  7. Draper, N. R., & Smith, H. (1998). Applied regression analysis (3rd ed.). New York: Wiley.CrossRefGoogle Scholar
  8. Faramarzi, A., Alani, A. M., & Javadi, A. A. (2014). An EPR-based self-learning approach to material modelling. Computers & Structures,137, 63–71.CrossRefGoogle Scholar
  9. Ghodsi, S. H., Kerachian, R., MalakpourEstalaki, S., Nikoo, M. R., & Zahmatkesh, Z. (2016). Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories. Journal of Hydrology,533, 200–212.CrossRefGoogle Scholar
  10. Giustolisi, O., Doglioni, A., Savic, D. A., & Webb, B. W. (2007). A multi-model approach to analysis of environmental phenomena. Environmental Modelling and Software,22(5), 674–682.CrossRefGoogle Scholar
  11. Harbaugh, A. W., Banta, E. R., Hill, M. C., & McDonald, M. G. (2000). MODFLOW-2000, the U. S. geological survey modular ground-water model-user guide to modularization concepts and the ground-water flow process. Open-file report. U. S. Geological Survey, (92), 134.Google Scholar
  12. Harne, S., Chaube, U. C., Sharma, S., Sharma, P., & Parkhya, S. (2006). Mathematical modelling of salt water transport and its control in groundwater. Natural and Science,4(4), 32–39.Google Scholar
  13. He, X., Højberg, A. L., Jørgensen, F., & Refsgaard, J. C. (2015). Assessing hydrological model predictive uncertainty using stochastically generated geological models. Hydrological Processes,29, 4293–4311.CrossRefGoogle Scholar
  14. Hine, D., & Hall, J. W. (2010). Information gap analysis of flood model uncertainties and regional frequency analysis. Water Resources Research,46(1), W01514.CrossRefGoogle Scholar
  15. Hussain, M. S., Javadi, A. A., Ahangar-Asr, A., & Farmani, R. (2015). A surrogate model for simulation–optimization of aquifer systems subjected to seawater intrusion. Journal of Hydrology,523, 542–554.CrossRefGoogle Scholar
  16. Javadi, A. A., Abd-Elhamid, H. F., & Farmani, R. (2011). A simulation-optimization model to control seawater intrusion in coastal aquifers using abstraction/recharge wells. International Journal for Numerical and Analytical Methods in Geomechanics, 36(16), 1757–1779.CrossRefGoogle Scholar
  17. Ketabchi, H., & Ataie-Ashtiani, B. (2015). Coastal groundwater optimization—Advances, challenges, and practical solutions. Hydrogeology Journal,23(6), 1129–1154.CrossRefGoogle Scholar
  18. Kourakos, G., & Mantoglou, A. (2009). Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Advances in Water Resources,32(4), 507–521.CrossRefGoogle Scholar
  19. Langevin, C. D., Thorne Jr, D. T., Dausman, A. M., Sukop, M. C., & Guo, W. (2008). SEAWAT version 4: A computer program for simulation of multi-species solute and heat transport (No. 6-A22). Geological Survey (US).Google Scholar
  20. Lin, H. J., Rechards, D. R., Talbot, C. A., Yeh, G. T., Cheng, J. R., Cheng, H. P., et al. (1997). A three-dimensional finite-element computer model for simulating density-dependent flow and transport in variable saturated media: version 3.1. Vicksburg, MS: US Army Engineering Research and Development Center.Google Scholar
  21. Masoumi, F., & Kerachian, R. (2008). Assessment of the groundwater salinity monitoring network of the Tehran region: Application of the discrete entropy theory. Water Science and Technology, 58(4), 765–771.CrossRefGoogle Scholar
  22. Matrosov, E. S., Woods, A. M., & Harou, J. J. (2013). Robust decision making and info-gap decision theory for water resource system planning. Journal of Hydrology,494, 43–58.CrossRefGoogle Scholar
  23. Qahman, K., Larabi, A., Ouazar, D., Ahmed, N. A. J. I., & Alexander, H. D. C. (2009). Optimal extraction of groundwater in Gaza coastal aquifer. Journal of Water Resource and Protection,1(04), 249.CrossRefGoogle Scholar
  24. Qom Regional Water Company. (2011). The quality and quantity study of groundwater flow in the Qom-Kahak aquifer. Technical report(in Persian).Google Scholar
  25. Quinlan, R. J. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence.Google Scholar
  26. Rajabi, A. M. (2018). A numerical study on land subsidence due to extensive overexploitation of groundwater in Aliabad plain, Qom-Iran. Natural Hazards,93(2), 1085–1103.CrossRefGoogle Scholar
  27. Ranjbar, A., & Mahjouri, N. (2018). Development of an efficient surrogate model based on aquifer dimensions to prevent seawater intrusion in anisotropic coastal aquifers, case study: the Qom aquifer in Iran. Environmental Earth Sciences,77(11), 418.CrossRefGoogle Scholar
  28. Rastogi, A. K., Choi, G. W., & Ukarande, S. K. (2004). Diffused interface model to prevent ingress of sea water in multi-layer coastal aquifers. Journal of Spatial Hydrology,4(2), 1–31.Google Scholar
  29. Refsgaard, J. C., Christensen, S., Sonnenborg, D. S., Hojberg, A. L., & Troldborg, L. (2012). Review of strategies for handling geological uncertainty in groundwater flow and transport modeling. Advances in Water Resources,36, 36–50.CrossRefGoogle Scholar
  30. Roach, T., Kapelan, Z., & Ledbetter, R. (2015). Comparison of info-gap and robust optimisation methods for integrated water resource management under severe uncertainty. Procedia Engineering,119, 874–883.CrossRefGoogle Scholar
  31. Scholze, O., Hillmer, G., & Schneider, W. (2002). Protection of the groundwater resources of Metropolis CEBU (Philippines) in consideration of saltwater intrusion into the coastal aquifer. In 17th saltwater intrusion meeting, Delft, The Netherlands.Google Scholar
  32. Sedki, A., & Ouazar, D. (2011). Simulation–optimization modeling for sustainable groundwater development: A Moroccan coastal aquifer case study. Water Resources Management,25(11), 2855–2875.CrossRefGoogle Scholar
  33. Sherif, M. M., & Hamza, K. I. (2001). Mitigation of seawater intrusion by pumping brackish water. Transport in Porous Media,43(1), 29–44.CrossRefGoogle Scholar
  34. Sherif, M., & Kacimov, A. (2008). Pumping of brackish and saline water in coastal aquifers: An effective tool for alleviation of seawater intrusion. In 20th Salt Water Intrusion Meeting (SWIM), Naples, Florida, USA.Google Scholar
  35. Soltani, M., Kerachian, R., Nikoo, M. R., & Noory, H. (2018). Planning for agricultural return flow allocation: Application of info-gap decision theory and a nonlinear CVaR-based optimization model. Environmental Science and Pollution Research, 25(25), 25115–25129.CrossRefGoogle Scholar
  36. Sreekanth, J., & Datta, B. (2010). Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. Journal of Hydrology,393(3), 245–256.CrossRefGoogle Scholar
  37. Voss, C. I., & Provost, A. M. (2010). SUTRA: A model for saturated–unsaturated, variable-density groundwater flow with solute or energy transport. US Geological Survey on water resources, investigations report 02-4231. Google Scholar
  38. Wang, Y., & Witten, I. H. (1996). Induction of model trees for predicting continuous classes. (Working paper 96/23). Hamilton: Department of Computer Science, University of Waikato.Google Scholar
  39. Werner, A. D., Bakker, M., Post, V. E., Vandenbohede, A., Lu, C., Ataie-Ashtiani, B., et al. (2013). Seawater intrusion processes, investigation and management: Recent advances and future challenges. Advances in Water Resources,51, 3–26.CrossRefGoogle Scholar
  40. Zheng, C., & Wang, P. P. (1999). MT3DMS: A modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems. Documentation and user’s guide. Tuscaloosa: Alabama University.Google Scholar
  41. Zischg, J., Goncalves, M. L., Bacchin, T. K., Leonhardt, G., Viklander, M., van Timmeren, A., et al. (2017). Info-Gap robustness pathway method for transitioning of urban drainage systems under deep uncertainties. Water Science and Technology,76(5), 1272–1281.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.Faculty of Civil EngineeringK. N. Toosi University of TechnologyTehranIran

Personalised recommendations