A fuzzy game theoretic approach for groundwater resources management: Application of Rubinstein Bargaining Theory

Developing optimal operating policies for conjunctive use of surface and groundwater resources when different stakeholders with conflicting objectives are involved, is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this study, a new methodology is proposed to resolve the conflict of interests among water users, water supply and environmental protection agencies which are involved in a problem of conjunctive use of surface and groundwater resources. In order to develop a Pareto front among objectives, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used. To incorporate the objectives related to groundwater table fluctuations and groundwater quality, the MODFLOW and MT3D groundwater quantity and quality simulation models and the optimization model are embedded. The best non-dominated solution on the Pareto front is selected using the Rubinstein Sequential Bargaining Theory (RSBT). The Fuzzy Set Theory is also utilized to better define the utility functions of decision makers. To evaluate the efficiency of the proposed methodology, it is applied to the conjunctive use of surface and groundwater resources in the Tehran metropolitan area, Iran.

[1]  Mohammad Karamouz,et al.  Monthly Water Resources and Irrigation Planning: Case Study of Conjunctive Use of Surface and Groundwater Resources , 2004 .

[2]  Ken Binmore,et al.  Noncooperative models of bargaining , 1992 .

[3]  C. Zheng A Modular Three-Dimensional Transport Model for Simulation of Advection, Dispersion and Chemical Reaction of Contaminants in Groundwater Systems , 1990 .

[4]  Stefan Napel,et al.  Bilateral Bargaining - Theory and Applications , 2002, Lecture notes in economics and mathematical systems.

[5]  Mohammad Karamouz,et al.  A stochastic conflict resolution model for water quality management in reservoir–river systems , 2007 .

[6]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[7]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[8]  Reza Kerachian,et al.  A simplified model for reservoir operation considering the water quality issues: Application of the Young conflict resolution theory , 2008, Environmental monitoring and assessment.

[9]  David E. Goldberg,et al.  Designing a competent simple genetic algorithm for search and optimization , 2000 .

[10]  Reza Kerachian,et al.  Developing a Conflict Resolution Model for Groundwater Quantity and Quality Management: A Case Study , 2008 .

[11]  Mohammad Karamouz,et al.  Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources , 2007 .

[12]  S. Raquel,et al.  Application of game theory for a groundwater conflict in Mexico. , 2007, Journal of environmental management.

[13]  Mohammad Karamouz,et al.  A game theoretic approach for trading discharge permits in rivers. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  A. Rubinstein,et al.  Bargaining and Markets , 1991 .

[15]  J. Bear Hydraulics of Groundwater , 1979 .

[16]  A. Rubinstein Perfect Equilibrium in a Bargaining Model , 1982 .

[17]  Reza Kerachian,et al.  A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study , 2009, Environmental management.

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[20]  S. Ranji Ranjithan,et al.  Evolutionary Multiobjective Optimization in Watershed Water Quality Management , 2003, EMO.

[21]  Najmeh Mahjouri,et al.  A game theoretic approach for interbasin water resources allocation considering the water quality issues , 2010, Environmental monitoring and assessment.

[22]  T. J. Buchanan,et al.  Techniques of Water-Resources Investigations of the United States Geological Survey , 1968 .

[23]  Jason F. Shogren,et al.  How probability weighting affects participation in water markets , 2006 .

[24]  S. Murty Bhallamudi,et al.  Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II , 2006 .

[25]  D. Savić,et al.  Multiobjective design of water distribution systems under uncertainty , 2005 .

[26]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[27]  Reza Kerachian,et al.  A stochastic conflict resolution model for trading pollutant discharge permits in river systems , 2009, Environmental monitoring and assessment.

[28]  Ivo F. Sbalzariniy,et al.  Multiobjective optimization using evolutionary algorithms , 2000 .

[29]  S. Hart,et al.  HANDBOOK OF GAME THEORY , 2011 .

[30]  W. Yeh,et al.  Uncertainty Analysis in Contaminated Aquifer Management , 2002 .

[31]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[32]  Mohammad Karamouz,et al.  Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach , 2006 .

[33]  David E. Goldberg,et al.  Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm‐II , 2003 .

[34]  Patrick M. Reed,et al.  Striking the Balance: Long-Term Groundwater Monitoring Design for Conflicting Objectives , 2004 .