Water Resources Management

, Volume 29, Issue 14, pp 5353–5375 | Cite as

Multi-objective Optimization Tool for Integrated Groundwater Management

  • Issam Nouiri
  • Muluneh Yitayew
  • Jobst Maßmann
  • Jamila Tarhouni
Article

Abstract

Integrated water resources management requires demands from agriculture, industry, and domestic users be met with the available supply with full considerations to water quality, cost and the environment. Thus, optimal allocation of available water resources is the challenge faced by water managers and policy makers to meet demands. With this in mind, a new tool called ALL_WATER_gw was developed for groundwater management within the framework of the WEAP-MODFLOW Decision Support System. It takes into account satisfaction of demand, minimization of water cost and maximal drawdown, as well as meeting water salinity restrictions. A Multi-Objective Genetic Algorithm (MOGA) and the PARETO optimality approaches were used to handle the formulated problem. Sensitivity analysis based on a pilot study showed that the MOGA parameters have strong impacts on the efficiency and the robustness of the developed tool. The results also demonstrated the tool’s capabilities to identify optimal solutions and support groundwater management decisions.

Keywords

Groundwater Management Multi-objective Optimization Genetic algorithm 

References

  1. Ayvaz TM (2009) Application of harmony search algorithm to the solution of groundwater management models. Adv Water Resour 32:916–924CrossRefGoogle Scholar
  2. Back T, Fogel DB, Michalewicz T (2000) Evolutionary computation 1: basic algorithms and operators. Institute of Physics Publishing, United KingdomCrossRefGoogle Scholar
  3. Bates BC, Kundzewicz ZW, Wu S, Palutikof JP (eds) (2008) Climate change and water. Technical paper of the intergovernmental panel on climate change. IPCC Secretariat, Geneva, 210 ppGoogle Scholar
  4. Chang LC (2008) Guiding rational reservoir flood operation using penalty-type genetic algorithm. J Hydrol 354:65–74CrossRefGoogle Scholar
  5. Coelho AC, Labadie JW, Fontane DG (2012) Multicriteria decision support system for regionalization of integrated water resources management. Water Resour Manag 26:1325–1346CrossRefGoogle Scholar
  6. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast Non-dominated genetic algorithm for multi-objective optimisation : NSGA-II. KanGAL report No. 200001. Indian Institute of Technology, KanpurGoogle Scholar
  7. Droubi A, Al-Sibai M, Abdallah A, Zahra S, Obeissi M, Wolfer J, Huber M, Hennings V, Schelkes KA (2008) Decision support system (DSS) for water resources management, −design and results from a pilot study in Syria. In: Zereini F, Hötzl H (Eds.): Climatic changes and water resources in the middle east and north africa. Springer, p 199–225Google Scholar
  8. Esquivel S, Leiva HA, Gallard RH (1999) Multiplicity in genetic algorithms to face multicriteria optimization. In: Congress on evolutionary computation. IEEE Service Centre, WashingtonGoogle Scholar
  9. Fonseca CR, Esteller MV, Díaz-Delgado C (2013) Territorial approach to increased energy consumption of water extraction from depletion of a highlands Mexican aquifer. J Environ Manag 128(2013):920–930CrossRefGoogle Scholar
  10. Fu G (2008) A fuzzy optimization method for multicriteria decision making: an application to reservoir flood control operation. Expert Syst Appl 34:145–149CrossRefGoogle Scholar
  11. Gholami V, Yousefi Z, Rostami HZ (2010) Modeling of ground water salinity on the Caspian southern coasts. Water Resour Manag 24:1415–1424CrossRefGoogle Scholar
  12. Giupponi C (2007) Decision support systems for implementing the european water framework directive: the MULINO approach. Environ Model Softw 22:248–258CrossRefGoogle Scholar
  13. Goldberg DE (1991) Genetic algorithms. Addison-WesleyGoogle Scholar
  14. GWP - Global Water Partnership (2000) TAC background papers No. 4. Integrated Water Resources Management, GWP, DenmarkGoogle Scholar
  15. Haddad R, Nouiri I, Alshihabi O, Maßmann J, Huber M, Laghouane A, Yahiaoui H, Tarhouni J (2013) A decision support system to manage the groundwater of the zeuss koutine aquifer using the WEAP-MODFLOW framework. Water Resour Manag 27:1981–2000. doi:10.1007/s11269-013-0266-7 CrossRefGoogle Scholar
  16. Harbaugh AW (2005) MODFLOW-2005, the U.S. geological survey modularGoogle Scholar
  17. IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New YorkGoogle Scholar
  18. Khare D, Jat MK, Deva SV (2007) Assessment of water resources allocation options: conjunctive use planning in a link canal command. Resour Conserv Recycl 51:487–506CrossRefGoogle Scholar
  19. Knowles J, Corne D (1999) The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. Proceedings of the 1999 congress on evolutionary computation (CEC'99). Volume 1, 98–105Google Scholar
  20. Koutsoyiannis D, Karavokiros G, Efstratiadis A, Mamassis N, Koukouvinos A, Christofides A (2003) A decision support system for the management of the water resource system of Athens. Phys Chem Earth, Parts A B C 28:599–609CrossRefGoogle Scholar
  21. Leiva HA, Esquivel SC, Gallard RH (2000) Miltiplicity and local search in, evolutionary algorithms to build the pareto front. Proceedings of the XX international conf. of the chilean computer science society (SCCC’00). 0-7695-0810-1/00. IEEE Service CentreGoogle Scholar
  22. Letcher RA, Croke BFW, Jakeman AJ (2007) Integrated assessment modelling for water resource allocation and management: a generalised conceptual framework. Environ Model Softw 22:733–742CrossRefGoogle Scholar
  23. Li YP, Huang GH, Nie SL, Liu L (2007) Inexact multistage stochastic integer programming for water resources management under uncertainty. J Environ Manag 88:93–107CrossRefGoogle Scholar
  24. Lis J, Eiben AE (1997) Multi-sexual genetic algorithm for multiobjective optimization. 4thInternational conf. on evolutionary computation (ICEC’97). Indiapolis, USA, p 59–64Google Scholar
  25. Liu S, Gikas P, Papageorgiou LG (2010) An optimisation-based approach for integrated water resources management. Comput Aided Chem Eng 28:1075–1080CrossRefGoogle Scholar
  26. Maßmann J, Wolfer J, Huber M, Schelkes K, Hennings V, Droubi A, Al-Sibai M (2012) WEAP-MODFLOW as a decision support system (DSS) for integrated water resources management: Design of the coupled model and results from a pilot study in Syria. In: Maloszewski P, Witczak S, Malina G (eds.): Groundwater quality sustainability. IAH selected papers. CRC PressGoogle Scholar
  27. Mongelli G, Monni S, Oggiano G, Paternoster M, Sinisi R (2013) Tracing groundwater salinization processes in coastal aquifers: a hydrogeochemical and isotopic approach in the Na-Cl brackish waters of northwestern Sardinia. Italy Hydrol Earth Syst Sci 17:2917–2928CrossRefGoogle Scholar
  28. Moradi-Jalal M, Haddad OB, Karney BW, Marin MA (2007) Reservoir operation in assigning optimal multi-crop irrigation areas. Agric Water Manag 90:149–159CrossRefGoogle Scholar
  29. Mysiak J, Giupponi C, Rosato P (2005) Towards the development of a decision support system for water resource management. Environ Model Softw 20:203–214CrossRefGoogle Scholar
  30. Nouiri I (2011) ALL_WATER_gw user guide, 37 pGoogle Scholar
  31. Nouiri I (2014) Multi-objective tool to optimize the water resources management using genetic algorithm and the pareto optimality concept. Water Resour Manag 28:2885–290CrossRefGoogle Scholar
  32. Pahl-Wostl C (2007) The implications of complexity for integrated resources management. Environ Model Softw 22:561–569CrossRefGoogle Scholar
  33. Prato T, Herath G (2007) Multiple-criteria decision analysis for integrated catchment management. Ecol Econ 63:627–632CrossRefGoogle Scholar
  34. Rees HG, Holmes MGR, Fry MJ, Young AR, Pitson DG, Kansakar SR (2006) An integrated water resource management tool for the Himalayan region. Environ Model Softw 21:1001–1012CrossRefGoogle Scholar
  35. 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
  36. SEI (2008) WEAP tutorial, 228 pGoogle Scholar
  37. Singh A (2014) Optimization modelling for seawater intrusion management. J Hydrol 508(16):43–52CrossRefGoogle Scholar
  38. Van Cauwenbergh N, Pinte D, Tilmant A, Frances I, Pulido-Bosch A et al (2008) Multi-objective, multiple participant decision support for water management in the Andarax catchment. Almeria Environ Geol 54:479–489CrossRefGoogle Scholar
  39. Vasan A, Raju KS (2008) Comparative analysis of simulated annealing, simulated quenching and genetic algorithms for optimal reservoir operation. Appl Soft Comput J. doi:10.1016/j.asoc.2007.09.002 Google Scholar
  40. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms – A comparative case study. Proceeding of parallel problem solving nature v. Amsterdam. 292–301Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Issam Nouiri
    • 1
  • Muluneh Yitayew
    • 2
  • Jobst Maßmann
    • 3
  • Jamila Tarhouni
    • 1
  1. 1.Laboratory of Water Sciences and TechnologiesNational Institute of Agronomy of TunisiaTunisTunisia
  2. 2.Agricultural and Biosystems Engineering DepartmentThe University of ArizonaTucsonUSA
  3. 3.Federal Institute for Geosciences and Natural Resources (BGR)HannoverGermany

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