An agent-based-nash modeling framework for sustainable groundwater management: A case study
Authored by Masih Akhbari, Saber Farhadi, Mohammad Reza Nikoo, Gholam Reza Rakhshandehroo, Mohammad Reza Alizadeh
Date Published: 2016
DOI: 10.1016/j.agwat.2016.08.018
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Abstract
An agent-based-Nash modeling framework has been developed to find a
sustainable solution for groundwater management in Daryan Aquifer, Fars
Province, Iran. This framework also includes a MODFLOW simulation model, an Artificial Neural Network (ANN), and a Non-dominated Sorting Genetic
Algorithm II (NSGA-II) optimization model. Groundwater state was
simulated using MODFLOW and it was calibrated based on the measured data
provided by Regional Water Organization (RWO) of Fars Province. In order
to reduce the computational time, an ANN was trained and validated based
on the input-output data of the MODFLOW model to estimate groundwater
level. The validated ANN was linked to a nonhomogeneous elitist NSGA-II
multi-objective optimization model to find a Pareto optimal front among
the three objectives of reducing irrigation water deficit, increasing
equity in water allocation, and reducing groundwater drawdown, as the
objectives of the three main groundwater resource stakeholders; farmers, the government executive sector, and the environmental protection
institutes. The Nash bargaining model was applied to the optimal
solutions in order to find a compromise among the stakeholders. Social
influential factors in the study environment, and policy mechanisms to
encourage agents to cooperate with the management decisions were
implemented in the agent-based model. These factors include training, incentives, penalties, and social norming (neighbors' impacts), as well
as considering the executive and judicial systems. After application of
the agent-based model, computed optimum solutions were modified
according to social conditions. Finally, the Nash bargaining model was
used again to find a compromise among modified optimal objectives of the
stakeholders. Implementation of this solution led to 58.3\% less water
extraction and approximately 3 m water level uplift. (C) 2016 Elsevier
B.V. All rights reserved.
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