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An agent-based-nash modeling framework for sustainable groundwater management: A case study

Author

Listed:
  • Farhadi, Saber
  • Nikoo, Mohammad Reza
  • Rakhshandehroo, Gholam Reza
  • Akhbari, Masih
  • Alizadeh, Mohammad Reza

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 3m water level uplift.

Suggested Citation

  • Farhadi, Saber & Nikoo, Mohammad Reza & Rakhshandehroo, Gholam Reza & Akhbari, Masih & Alizadeh, Mohammad Reza, 2016. "An agent-based-nash modeling framework for sustainable groundwater management: A case study," Agricultural Water Management, Elsevier, vol. 177(C), pages 348-358.
    • Handle: RePEc:eee:agiwat:v:177:y:2016:i:c:p:348-358
    DOI: 10.1016/j.agwat.2016.08.018
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    References listed on IDEAS

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    Cited by:

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    3. Xiaowei Wang & Jingli Shao & Yali Cui & Qiulan Zhang, 2020. "Application of a Surrogate Model for a Groundwater Numerical Simulation Model for Determination of the Annual Control Index of the Groundwater Table in China," Sustainability, MDPI, vol. 12(14), pages 1-16, July.
    4. Ghazali, Mahboubeh & Honar, Tooraj & Nikoo, Mohammad Reza, 2018. "A hybrid TOPSIS-agent-based framework for reducing the water demand requested by stakeholders with considering the agents’ characteristics and optimization of cropping pattern," Agricultural Water Management, Elsevier, vol. 199(C), pages 71-85.
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    6. Alireza Nouri & Bahram Saghafian & Majid Delavar & Mohammad Reza Bazargan-Lari, 2019. "Agent-Based Modeling for Evaluation of Crop Pattern and Water Management Policies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3707-3720, September.
    7. Tesfaye Woldeyohanes & Arnim Kuhn & Thomas Heckelei & Lalisa Duguma, 2021. "Modeling Non-Cooperative Water Use in River Basins," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
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    11. Bahrami, Nafiseh & Afshar, Abbas & Afshar, Mohammad Hadi, 2022. "An agent-based framework for simulating interactions between reservoir operators and farmers for reservoir management with dynamic demands," Agricultural Water Management, Elsevier, vol. 259(C).
    12. Anbari, Mohammad Javad & Zarghami, Mahdi & Nadiri, Ata-Allah, 2021. "An uncertain agent-based model for socio-ecological simulation of groundwater use in irrigation: A case study of Lake Urmia Basin, Iran," Agricultural Water Management, Elsevier, vol. 249(C).
    13. Tsai, Wen-Ping & Cheng, Chung-Lien & Uen, Tinn-Shuan & Zhou, Yanlai & Chang, Fi-John, 2019. "Drought mitigation under urbanization through an intelligent water allocation system," Agricultural Water Management, Elsevier, vol. 213(C), pages 87-96.
    14. Abbas Mirzaei & Mansour Zibaei, 2021. "Water Conflict Management between Agriculture and Wetland under Climate Change: Application of Economic-Hydrological-Behavioral Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 1-21, January.
    15. Ali Reza Nafarzadegan & Hassan Vagharfard & Mohammad Reza Nikoo & Ahmad Nohegar, 2018. "Socially-Optimal and Nash Pareto-Based Alternatives for Water Allocation under Uncertainty: an Approach and Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 2985-3000, July.

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