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Water Resources Management

, Volume 28, Issue 10, pp 2885–2901 | Cite as

Multi-Objective tool to optimize the Water Resources Management using Genetic Algorithm and the Pareto Optimality Concept

  • Issam NouiriEmail author
Article

Abstract

This paper examines the development of a multi-objective tool, called “ALL_WATER”, in optimizing Water Resources Management. The objectives of satisfying demand and reducing costs were taken into consideration while at the same time respecting water salinity requirements and hydraulic constraints. A Multi-Objective Genetic Algorithm (MOGA) and the PARETO optimality concept were used to resolve the formulated problem. The tool developed was used to help optimize the daily management schedule of a real case study in Tunisia. The hydraulic system is made up of three surface water sources, one demand site, two transfer links and three supply links. Within a short computation time, a PARETO front was identified made up of a set of 72 optimal solutions. The modeling approach and the decision-making flexibility, both shown in the case study, prove that the developed tool is able to efficiently identify a set of optimal solutions on a PARETO front. The developed tool will be able to be used for a large variety of water management problems.

Keywords

ALL_WATER Water Management Optimization Genetic Algorithm Tunisia 

Notes

Acknowledgments

The authors would like to thank the National Institute of Agronomy of Tunisia (INAT) and the Tunisian National Drinking Water Utility for their support of this work.

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. Cai X (2007) Implementation of holistic water resources-economic optimization models for river basin management e Reflective experiences. Environ Model Softw 23:2–18CrossRefGoogle Scholar
  4. Chang L (2008) Guiding rational reservoir flood operation using penalty-type genetic algorithm. J Hydrol 354:65–74CrossRefGoogle Scholar
  5. Collette Y and Siarry P (2003) Multiobjective Optimization: Principles and Case Studies. Springer.Google Scholar
  6. Deb K, Agrawal S, Pratap A and Meyarivan T (2000) A Fast Non-Dominated Genetic Algorithm for Multi-Objective Optimisation : NSGA-II. KanGAL Report No. 200001, Indian Institute of Technology, Kanpur, India.Google Scholar
  7. Dvarioniene J, Stasiskiene Z (2007) Integrated water resource management model for process industry in Lithuania. J Clean Prod 15:950–957CrossRefGoogle Scholar
  8. Esquivel S, Leiva HA and Gallard RH (1999) Multiplicity in genetic algorithms to face multicriteria optimization. Congress on Evolutionary Computation. Washington D.C., July 1999. IEEE Service Centre.Google Scholar
  9. Gaivoronski AA, Sechi GM, Zuddas P (2011) Balancing cost-risk in management optimization of water resource systems under uncertainty. Physics and Chemistry. doi: 10.1016/j.pce.2011.05.015 Google Scholar
  10. Giupponi C (2007) Decision Support Systems for implementing the European Water Framework Directive: The MULINO approach. Environ Model Softw 22:248–258CrossRefGoogle Scholar
  11. Goldberg DE (1991) Genetic Algorithms. Addison-Wesley.Google Scholar
  12. Hrstka O, Kucerova A (2004) Improvements of real coded genetic algorithms based on differential operators preventing premature convergence. Adv Eng Softw 35:237–246CrossRefGoogle Scholar
  13. Ioris AAR, Hunter C, Walker S (2008) The development and application of water management sustainability indicators in Brazil and Scotland. J Environ Manag 88:1190–1201CrossRefGoogle Scholar
  14. Khare D, Jat MK, Deva Sunder V (2007) Assessment of water resources allocation options: Conjunctive use planning in a link canal command. Resour Conserv Recycl 51:487–506CrossRefGoogle Scholar
  15. Knowles J and 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), 1, 98–105.Google Scholar
  16. Koutsoyiannis D, Karavokiros G, Efstratiadis A. Mamassis N, Koukouvinos A and Christofides A (2003) A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, Parts A/B/C, 28, 599–609Google Scholar
  17. Leiva HA, Esquivel SC and 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 Centre.Google Scholar
  18. Letcher RA, Croke BFW, Jakeman AJ (2007) Integrated assessment modelling for water resource allocation and management: A generalized conceptual framework. Environ Model Softw 22:733–742CrossRefGoogle Scholar
  19. 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
  20. Lis J and Eiben A E (1997) Multi-sexual genetic algorithm for multiobjective optimization. 4th International conf. On evolutionary computation (ICEC’97). Indiapolis, USA, 59–64.Google Scholar
  21. Liu S, Gikas P, Papageorgiou LG (2010) An Optimisation-based Approach for Integrated Water Resources Management. Computer & Chemical Engineering 28:1075–1080Google Scholar
  22. Liu S, Konstantopoulou F, Gikas P, Papageorgiou LG (2011) A mixed integer optimisation approach for integrated water resources management. Comput Chem Eng 35:858–875CrossRefGoogle Scholar
  23. 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
  24. 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
  25. Prato T, Herath G (2007) Multiple-criteria decision analysis for integrated catchment management. ECOLOGICAL ECONOMICS 63:627–632CrossRefGoogle Scholar
  26. 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
  27. Ren JL, Lyu PH, Wu XM, Ma FC, Wang ZZ, Yang G (2013) An Informetric Profile of Water Resources Management Literatures. Water Resour Manage 27:4679–4696CrossRefGoogle Scholar
  28. Sechi GM, Sulis A (2007) Multi-Reservoir System Optimization using Chlorophyll-a Trophic Indexes. Water Resour Manag 21:849–860CrossRefGoogle Scholar
  29. Sedki A, Ouazar D (2011) Simulation-Optimization Modeling for Sustainable Groundwater Development: A Moroccan Coastal Aquifer Case Study. Water Resour Manag 25:2855–2875CrossRefGoogle Scholar
  30. 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
  31. Yazdi J, Salehi Neyshabouri SAA (2012) A Simulation-Based Optimization Model for Flood Management on a Watershed Scale. Water Resour Manage 26:4569–4586CrossRefGoogle Scholar
  32. Zhang C, Wang G, Peng Y (2012) Tang G and Liang G (2012) A Negotiation-Based Multi-Objective, Multi-Party Decision-Making Model for Inter-Basin Water Transfer Scheme Optimization. Water Resour Manage 26:4029–4038CrossRefGoogle Scholar
  33. 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 2014

Authors and Affiliations

  1. 1.National Institute of Agronomy of TunisiaTunisTunisia

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