Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

Abstract Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

[1]  Mohammad Reza Nikoo,et al.  Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study , 2017, Water Resources Management.

[2]  Mahdi Zarghami,et al.  Soft computing of the Borda count by fuzzy linguistic quantifiers , 2009, Appl. Soft Comput..

[3]  H. Nurmi.  Approaches to collective decision making with fuzzy preference relations , 1981 .

[4]  K. Hipel,et al.  Modeling the Caspian Sea Negotiations , 2010 .

[5]  D. C. Morais,et al.  Group decision making on water resources based on analysis of individual rankings , 2012 .

[6]  Nima Ehsani,et al.  A neural network based general reservoir operation scheme , 2016, Stochastic Environmental Research and Risk Assessment.

[7]  P. C. de Ruiter,et al.  Using a groundwater quality negotiation support system to change land-use management near a drinking-water abstraction in the Netherlands , 2008 .

[8]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[9]  Slawomir Zadrozny,et al.  On Group Decision Making, Consensus Reaching, Voting and Voting Paradoxes under Fuzzy Preferences and a Fuzzy Majority: A Survey and some Perspectives , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[10]  S. Zahariev,et al.  An approach to group choice with fuzzy preference relations , 1987 .

[11]  Babak Abbasi,et al.  Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences , 2015, Environ. Model. Softw..

[12]  Mohammad Reza Nikoo,et al.  An agent-based-nash modeling framework for sustainable groundwater management: A case study , 2016 .

[13]  Dominique Salameh,et al.  Short-term relationships between emergency hospital admissions for respiratory and cardiovascular diseases and fine particulate air pollution in Beirut, Lebanon , 2015, Environmental Monitoring and Assessment.

[14]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[15]  Najmeh Mahjouri,et al.  Waste Load Allocation in Rivers using Fallback Bargaining , 2013, Water Resources Management.

[16]  Michael Hanss,et al.  The Extended Transformation Method For The Simulation And Analysis Of Fuzzy-Parameterized Models , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[17]  Michael Hanss,et al.  On the reliability of the influence measure in the transformation method of fuzzy arithmetic , 2004, Fuzzy Sets Syst..

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

[19]  Richard Foltz,et al.  Iran's Water Crisis: Cultural, Political, and Ethical Dimensions , 2002 .

[20]  José Luis García-Lapresta,et al.  Borda Count Versus Approval Voting: A Fuzzy Approach , 2002 .

[21]  J. Mas-Pla,et al.  Identifying the effects of human pressure on groundwater quality to support water management strategies in coastal regions: a multi-tracer and statistical approach (Bou-Areg region, Morocco). , 2014, The Science of the total environment.

[22]  J. Kacprzyk,et al.  Group decision making and consensus under fuzzy preferences and fuzzy majority , 1992 .

[23]  Yolanda Martínez,et al.  Water allocation by social choice rules: The case of sequential rules , 2008 .

[24]  Konstantinos Voudouris,et al.  Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: Modifying the DRASTIC method using quantitative parameters , 2015 .

[25]  Mohammad Reza Nikoo,et al.  Stakeholder engagement in multi-objective optimization of water quality monitoring network, case study: Karkheh Dam reservoir , 2017 .

[26]  K. Madani,et al.  Bargaining over the Caspian Sea- the Largest Lake on the Earth , 2008 .

[27]  Paul P. Wang,et al.  MT3DMS: A Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Groundwater Systems; Documentation and User's Guide , 1999 .

[28]  Annika Kangas,et al.  Applying voting theory in natural resource management: a case of multiple-criteria group decision support. , 2002, Journal of environmental management.

[29]  P. Gleick.  The changing water paradigm. A look at twenty-first century water resources development. , 2000 .

[30]  Bojan Srdjevic,et al.  Linking analytic hierarchy process and social choice methods to support group decision-making in water management , 2007, Decis. Support Syst..

[31]  Arlen W. Harbaugh,et al.  User's documentation for MODFLOW-96, an update to the U.S. Geological Survey modular finite-difference ground-water flow model , 1996 .

[32]  E. Ostrom,et al.  The Struggle to Govern the Commons , 2003, Science.

[33]  Laura Read,et al.  Voting Under Uncertainty: A Stochastic Framework for Analyzing Group Decision Making Problems , 2014, Water Resources Management.

[34]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[35]  Najmeh Mahjouri,et al.  Waste load allocation in rivers under uncertainty: application of social choice procedures , 2015, Environmental Monitoring and Assessment.

[36]  Manuel Pulido-Velazquez,et al.  A hydro-economic modelling framework for optimal management of groundwater nitrate pollution from agriculture , 2009 .