Optimal Water Resources Management and Operation Based on Social Choice Procedures

Subject Areas : Subjects

محمدرضا علیزاده ¹ (دانشجوی کارشناسی ارشد بخش مهندسی عمران و محیط زیست، دانشگاه شیراز)
محمدرضا نیکو ² (استادیار بخش مهندسی عمران و محیط‌زیست، دانشکده‌ مهندسی، دانشگاه شیراز،)
غلامرضا رخشند‌رو ³ (استاد تمام بخش مهندسی عمران و محیط‌زیست، دانشکده‌ مهندسی، دانشگاه شیراز)
ناصر طالب‌بیدختی ⁴ (استاد تمام بخش مهندسی عمران و محیط‌زیست، دانشکده‌ مهندسی، دانشگاه شیراز)

Received: 2015-08-16 Accepted : 2015-08-16 Published : 2015-07-23

Keywords: MODFLOW, Water resource management, Social Choice Rule (SCR), NSGA-II multiobjective optimization model, MLP meta model,

Abstract :

Optimal management and operation of groundwater resources need to attract a great attention according to their special characteristics. Groundwater resources exploitation operation involves important issues such as conflicting and complex objectives, a large number of decision variables and different uncertainties. Conflict between goals of stakeholders is more obvious, especially when there are different stakeholders with conflicting objectives. The methodology presented in this paper is to determine an optimal allocation of groundwater resources with emphasis on resolving conflicts between involved stakeholders for developing appropriate policies based on the Social Choice Rule (SCR). In this study, an optimal groundwater resources allocation is determined by developing a simulation-optimization model. For this purpose, a meta-model based on Multi-Layer Perceptron (MLP) neural network is trained and validated using results of repeated executions of the groundwater simulation model (MODFLOW) to predict groundwater drawdowns. NSGA-II optimization model is utilized to determine the trade-off (Pareto fronts) between conflicting objectives. The best non-dominated solution on Pareto fronts is selected using the SCR as a compromise solution. A performance of the proposed methodology was analyzed by applying it to the case study of the Kavar-Maharlu aquifer in the Fars Province, Iran. Results indicated an acceptable performance of the proposed methodology for determining optimal groundwater allocation policy. By applying the suggested policy of proposed simulation-optimization model to the Kavar-Maharlu aquifer. An average annual withdrawal from the aquifer was reduced by 56% and reached 25.52 million cubic meters per year.

References:

1. اعلمی م. ت، ب، آقابالایی، م. ح، احمدی، س، فرزین. 1393. تخصیص بهینه نظام­های منابع آب با استفاده از سامانه پویا. فصلنامه علمی-پژوهشی مهندسی منابع آب. 7(23): 99-110


2. سازمان مدیریت و برنامه­ریزی، دستورالعمل تعیین محل و نظارت بر حفر چاه های آب در آبرفت و سازندهای سخت.. نشریه شماره 557، 226 صفحه


3. شرکت سهامی آب منطقه­ای فارس. 1391. مطالعات به هنگام­سازی اطلس منابع آب حوزه آبریز دریاچه­های طشک-بختگان و مهارلو. گزارش بیلان محدوده مطالعاتی کوار-مهارلو، 59 صفحه.


4. فلاح­مهدی­پور ا، ا، بزرگ­حداد، 1393. کاربرد شبیه حل­اختلاف نش در بهره­برداری چندمنظوره سامانه مخزن. فصلنامه علمی-پژوهشی مهندسی منابع آب. 7(22): 11-22


5. فلاح­مهدی­پور ا، ا، بزرگ­حداد، س، علیمحمدی، 1392. بهره­وری بهبنه از سامانه تلفیقی آبخوان- سد: رویکرد برنامه­ریزی ژنتیک. فصلنامه علمی-پژوهشی مهندسی منابع آب. 7(21): 51-66
   
   
   1. Barberà S, M, Jackson, and A. Neme. 1997. Strategy-proof allotment rules. Games and Economic Behavior. 18: 1–21.
   
   
   2. Barberà S. 2005. Strategy proofness. In: Arrow, KJ, Sen AK, Suzumara K. (Eds.). Handbook of Social Choice and Welfare. vol. II. Elsevier Science, Amsterdam.
   
   
   3. Bassett GW, and J. Persky. 1999. Robust Voting. Public Choice.99: 299-310.
   
   
   4. Bazargan-Lari MR, R, Kerachian, and Mansoori A. 2009. A conflict-resolution model for the conjunctive use of surface and groundwater resources that considers water-quality issues: A case study. Environmental Management. 43:470–482.
   
   
   5. Bogardi I and F. Szidarovszky. 1976. Application of game theory in water management, Applied Mathematical Modeling. 1(1):16-20.
   
   
   6. Deb K, S, Agrawal, A, Pratap, and T. Meyarivan, 2000. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. 6th International Conference Parallel Problem Solving from Nature PPSN VI, 18–20 September, Paris, France. 849-858
   
   
   7. Easter W, and R.Hearne 1995. Water markets and decentralized water resources management: international problems and opportunities. Water Resources Bulletin. 31 (1): 9–20.
   
   
   8. Ganji A, D, Khalili and M. Karamouz 2007. Development of stochastic dynamic Nash game model for reservoir operation. The symmetric stochastic model with perfect information. Advances in Water Resources. 30:528–542.
   
   
   9. Howe C, D, Schurmeier, and Jr, W.  Shaw. 1986. Innovative approaches to water allocation: the potential for water markets. Water Resources Research. 22 (4): 439–445.
   
   
   10. Kerachian, R, and M. Karamouz, 2006. Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach, Water Resources Research. 42(12): 1-17.
   
   
   11. Kerachian, R, and M. Karamouz, 2007. A stochastic conflict resolution model for water quality management in reservoir-river systems, Advances in Water Resources. 30(4): 866-882.
   
   
   12. Kerachian, R, M, Fallahnia, M. R,  Bazargan-Lari, A. Mansoori. and H. Sedghi, (2010). A fuzzy game theoretic approach for groundwater resources management: Application of Rubinstein bargaining theory, Journal of Resources, Conservation and Recycling. 54(10): 673-682.
   
   
   13. Lee, T, and A. Jouravlev, 1998. Los Precios, la Propiedad y los Mercados en la Asignación del Agua. CEPAL (Naciones Unidas), Santiago de Chile. Martínez Y., 2002.
   
   
   14. Loaiciga HA. 2004. Analytical game theoretic approach to groundwater extraction. J Hydrol. 297:22–33.
   
   
   15. Madani, K, OM, Rouhani, A. Mirchi. and S. Gholizadeh. 2013. A negotiation support system for resolving an international trans-boundary natural resource conflict. Environmental Modeling & Software. 51:240-249
   
   
   16. Martinez, Y. and E. Esteban.  2014. Social choice and groundwater management: application of the uniform rule, agricultural economics, ciencia e investigación agrarian. 41:153-162.
   
   
   17. Mianabadi, O, E, Mostert, M. Zarghami, and N. van de Giesen, 2014. A new bankruptcy method for conflict resolution in water resources allocation. Journal of Environmental Management. 144:152–159
   
   
   18. Nikoo, MR, I, Varjavand, R, Kerachian, M, Pirooz, A. Karimi, (2014) Multi-objective optimum design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models. Applied Ocean Research. 47:47–52.
   
   
   19. Niksokhan, MH, R. Kerachian. and M. Karamouz, 2009. A game theoretic approach for trading discharge permits in rivers, Water Science and Technology. 609(3): 793-804.
   
   
   20. Rafipour-Langeroudi M, R.Kerachian. and MR. Bazargan-Lari, 2014. Developing operating rules for conjunctive use of surface and groundwater considering the water quality issues. KSCE Journal of Civil Engineering. 18: 454-461
   
   
   21. Read, L, S, Mokhtari, K, Madani, M. Maimoun, and C. Hanks. 2013. A Multi-Participant, Multi-Criteria Analysis of Energy Supply Sources for Fairbanks, Alaska. World Environmental and Water Resources Congress. 2013: 1247-1257.
   
   
   22. Reed, PM, and BS. Minsker, 2004. Striking the balance: Long-term groundwater monitoring design for conflict objectives. Journal of Water Resources Planning and Management. 130(2):140-149.
   
   
   23. Saak, AE, and JM. Peterson, 2007. Groundwater use under incomplete information. Journal of Environmental Economics and Management. 54: 214-228.
   
   
   24. Salazar, R, F, Szidarouszky, EJr, Coppola, and A. Rojana. 2007. Application of game theory for groundwater conflict in Mexico”, Journal of Environmental Management. 84: 560-571.
   
   
   25. Sheikhmohammady, M, DM, Kilgour, and KW. Hipel. (2010) Modeling the Caspian Sea Negotiations. Group Decis Negot. 19:149–168.
   
   
   26. Sheikhmohammady, M, and K. Madani, 2008. Bargaining over the Caspian Sea—the largest lake on the earth. In: Babcock RW, Walton R (eds) Proceeding of the 2008 world environmental and water resources congress, Honolulu, Hawaii. ASCE:1-9.
   
   
   27. Sprumont, Y. 1991. The division problem with single peaked preferences: a characterization of the uniform allocation rules. Econometrica. 59: 509–519.
   
   
   28. Yandamuri, SRM, K. Srinivasan, and SM. Bhallamudi. 2006. Multi objective optimal waste load allocation models for rivers using nondominated sorting genetic algorithm-II. Journal of water resources planning and Management. 132:133–43.
   
   
   29. Yang, Z, Y, Zeng, Y, Cai, and Q. Tan. 2008. An integrated game-theory based model for trans-boundary water resources management in north china: A case study in the guanting reservoir basin (GRB), Beijing”, International Journal of Software Engineering and Knowledge Engineering. 18: 461-483.
   
   
   30. Young, HP, N, Okada, and T. Hashimoto 1982. Cost allocation in water resources development. Water Resources Research. 18: 463–475.