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Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study

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Abstract

Conflict-resolution models can be used as practical approaches to consider the contradictions and trade-offs between the involved stakeholders in integrated water resource management. These models are utilized to reach an optimal solution considering agents interactions. In this paper, a new methodology is developed based on multi-objective optimization model (NSGA-II), groundwater simulation model, M5P model tree, fallback bargaining procedures and social choice rules to determine the optimal groundwater management policies with an emphasis on resolving conflicts between stakeholders. By incorporating the multi-objective simulation-optimization model and bargaining methods, the optimal groundwater allocation policies are determined and the preferences of the stakeholders as well as social criteria such as justice are also considered. The obtained data set, based on Monte Carlo analysis of calibrated MODFLOW model, is used for training and validating the M5P meta-models. The validated M5P meta-models are linked with NSGA-II to determine the trade-off curve (Pareto front) for the objectives. Social choice rule and fallback bargaining methods, as conflict-resolution models, are applied to determine the best socio-optimal solution among stakeholders, and their results are compared. The effectiveness of the proposed methodology is verified in a case study of Darian aquifer, Fars province, Iran. Results indicated that the solutions obtained by the proposed conflict-resolution approaches have an appropriate applicability. Total groundwater withdrawal, after applying the optimal groundwater allocations, reduced to 20.85 MCM, resulting in a 4.62 m increase in the mean groundwater level throughout the aquifer.

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References

  • Ajmera T, Goyal MK (2015) Evaluation of soft computing algorithms for estimation of spatial transmissivity. Int J Water 9(2):168–177

    Article  Google Scholar 

  • Alfredson T, Cungu A (2008) Negotiation theory and practice: a review of literature. Esaypol on line resource materials for policymaking

  • Bassett GW, Persky J (1999) Robust voting. Public Choice 99:299–310

    Article  Google Scholar 

  • Bose D, Bose B (1995) Evaluation of alternatives for water project using a multi-objective decision matrix. Water Int 20:169–175

    Article  Google Scholar 

  • Brams SJ, Kilgour DM (2001) Fallback bargaining. Group Decis Negot 10:287–316

    Article  Google Scholar 

  • Brams SJ, Kilgour DM, Sanver M (2007) A minimax procedure for negotiating multilateral treaties. Diplomacy games. Springer, Berlin, pp 265–282

    Google Scholar 

  • Deb K, Pratap A, Agrawal S, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective: NSGA-II. In Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 846–858

  • Esteban E, Dinar A (2013) Cooperative management of groundwater resources in the presence of environmental externalities. Environ Resource Econ 54(3):443–469

    Article  Google Scholar 

  • Farhadi S, Nikoo MR, Rakhshandehroo GR, Akhbari M, Alizadeh MR (2015) An agent-based-nash modeling framework for sustainable groundwater management: a case study. Agric Water Manag 177:348–58

    Article  Google Scholar 

  • Fars regional water company (2013) Studies of updating the basin water resources atlas of Tashk-Bakhtegan and Maharloo lake, Darian water resource balance study, Iran, Technical Report

  • Ganji A, Khalili D, Karamouz M (2007) Development of stochastic dynamic Nash game model for reservoir operation: the symmetric stochastic model with perfect information. Adv Water Resour 30:528–542

    Article  Google Scholar 

  • Kontos YN, Katsifarakis KL (2012) Optimization of management of polluted fractured aquifers using genetic algorithms. Eur Water 40:31–42

    Google Scholar 

  • Loáiciga HA (2004) Analytic game theoretic approach to groundwater extraction. J Hydrol 297(1–4):22–33

    Article  Google Scholar 

  • Madani K, Shalikarian L, Naeeni STO (2011) Resolving hydro-environmental conflicts under uncertainty using fallback bargaining procedures. International Conference on Environment Science and Engineering (IPCBEE). IACSIT Press, Singapore

  • Madani K, Shalikarian L, Hamed A, Pierce T, Msowoya K, Rowney C (2015) Bargaining under uncertainty: a Monte-Carlo fallback bargaining method for predicting the likely outcomes of environmental conflicts. In Conflict Resolution in Water Resources and Environmental Management, pp. 201–212

  • Mahjouri N, Bizhani-Manzar M (2013) Waste load allocation in rivers using fallback bargaining. Water Resour Manag 27(7):2125–2136

    Article  Google Scholar 

  • Martinez Y, Esteban E (2014) Social choice and groundwater management: application of the uniform rule. CIENCIA Investig Agrar 41(2):153–162

    Google Scholar 

  • Nikolic VV, Simonovic SP (2015) Multi-method modeling framework for support of integrated water resources management. Environ Process 2(3):461–83

    Article  Google Scholar 

  • Nikoo MR, Kerachian R, Karimi A, Azadnia AA, Jafarzadegan K (2013) Optimal water and waste load allocation in reservoir–river systems: a case study. Environ Earth Sci 71(9):4127–4142

    Article  Google Scholar 

  • Nikoo MR, Varjavand I, Kerachian R, Pirooz M, Karimi A (2014) Multi-objective optimum design of double-layer perforated-wall breakwaters: application of NSGA-II and bargaining models. Appl Ocean Res 47:47–52

    Article  Google Scholar 

  • Parsapour-Moghaddam P, Abed-Elmdoust A, Kerachian R (2015) A heuristic evolutionary game theoretic methodology for conjunctive use of surface and groundwater resources. Water Resour Manag 29:3905–3918

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. Proceedings AI’92, 5th Australian Joint Conference on Artificial Intelligence, Adams & Sterling (eds) World Scientific, Singapore, 343–348

  • Read L, Mokhtari S, Madani K, Maimoun M, Hanks C (2013) A multi-participant, multi-criteria analysis of energy supply sources for Fairbanks, Alaska. World Environmental and Water Resources Congress, pp. 1247–1257

  • Reynolds A, Reilly B, Ellis A (2005) Electoral system design: the new international IDEA handbook. International Institute for Democracy and Electoral Assistance

  • Salazar R, Szidarovszky F, Coppola E Jr, Rojana A (2007) Application of game theory for a groundwater conflict in Mexico. J Environ Manage 84(4):560–571

    Article  Google Scholar 

  • Serrano R (2004) The theory of implementation of social choice rules. SIAM Rev 46(3):377–414

    Article  Google Scholar 

  • Shahriar MS, Kamruzzaman M, Beecham S (2014) Multiple resolution river flow time series modelling using machine learning methods. In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pp. 62, ACM

  • Sheikhmohammady M, Madani K (2008) Bargaining over the Caspian Sea-the largest lake on the earth. In Proceeding of the 2008 World Environmental and Water Resources Congress, Honolulu, Hawaii, pp. 1–9

  • Sheikhmohammady M, Kilgour DM, Hipel KW (2010) Modeling the Caspian Sea negotiations. Group Decis Negot 19(2):149–168

    Article  Google Scholar 

  • Shirangi E, Kerachian R, Bajestan MS (2008) A simplified model for reservoir operation considering the water quality issues: application of the Young conflict resolution theory. Environ Monit Assess 146(1–3):77–89

    Article  Google Scholar 

  • Sidiropoulos P, Mylopoulos N, Loukas A (2016) Reservoir-aquifer combined optimization for groundwater restoration: the case of Lake Karla watershed, Greece. Water Util J 12:17–26

    Google Scholar 

  • Walker WE, Loucks DP, Carr G (2015) Social responses to water management decisions. Environ Process 2(3):485–509

    Article  Google Scholar 

  • Wang Y, Witten IH (1997) Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European conference on machine learning, University of Economics, Prague

  • Yandamuri SRM, Srinivasan K, Bhallamudi SM (2006) Multi-objective optimal waste load allocation models for rivers using nondominated sorting genetic algorithm-II. J Water Resour Plann Manag 132(3):133–43

    Article  Google Scholar 

  • Young HP, Okada N, Hashimoto N (1982) Cost allocation in water resources development. Water Resour Res 18:463–475

    Article  Google Scholar 

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Correspondence to Mohammad Reza Nikoo.

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Alizadeh, M.R., Nikoo, M.R. & Rakhshandehroo, G.R. Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study. Water Resour Manage 31, 1457–1472 (2017). https://doi.org/10.1007/s11269-017-1588-7

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