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A Framework for Ground Water Management Based on Bayesian Network and MCDM Techniques

Author

Listed:
  • Abbas Roozbahani

    (University of Tehran)

  • Ebrahim Ebrahimi

    (University of Tehran)

  • Mohammad Ebrahim Banihabib

    (University of Tehran)

Abstract

Groundwater resources are steadily subjected to increasing water demands. The aquifers are considered as the most accessible source of fresh water. In recent years, they have been faced with severe water withdrawal in arid and semi-arid countries like Iran and thus some aquifers was considered as forbidden aquifers that it means the water withdrawal from these aquifers is unauthorized. Given a critical situation, groundwater resources management in the form of tools such as monitoring the level of the aquifers and developing the restoring scenarios is essential. Therefore, for this purpose, a framework has been developed based on prediction of groundwater level using Bayesian Networks (BNs) model. Furthermore, Multi Criteria Decision Making methods (MCDM) techniques proposed and employed for ranking of proposed groundwater management scenarios. This framework was evaluated for restoring the Birjand aquifer in Iran in different hydrological conditions. A probabilistic Dynamic BN was proposed for groundwater level prediction under uncertainties. After analyzing the obtained results, the applicable short term scenarios for groundwater management as well as appropriate economic, social and technical criteria were defined for decision making procedure. Then, using elicitation of decision makers’ opinions on the relative importance and performance of criteria, SAW, TOPSIS and PROMETHEE-II techniques were applied to rank the scenarios and the obtained results were aggregated by Borda method for final ranking of the scenarios. Lastly, the final results demonstrates the capability of the proposed framework for groundwater resources planning and management which can be employed for reducing the risk of aquifer level declining.

Suggested Citation

  • Abbas Roozbahani & Ebrahim Ebrahimi & Mohammad Ebrahim Banihabib, 2018. "A Framework for Ground Water Management Based on Bayesian Network and MCDM Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4985-5005, December.
  • Handle: RePEc:spr:waterr:v:32:y:2018:i:15:d:10.1007_s11269-018-2118-y
    DOI: 10.1007/s11269-018-2118-y
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    References listed on IDEAS

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    3. Amir Homayoun Sarfaraz & Amir Karbassi Yazdi & Thomas Hanne & Peter Fernandes Wanke & Raheleh Sadat Hosseini, 2023. "Assessing repair and maintenance efficiency for water suppliers: a novel hybrid USBM-FIS framework," Operations Management Research, Springer, vol. 16(3), pages 1321-1342, September.
    4. Geetha, Selvaraj & Jeon, JeongHwan, 2023. "Stratified network mapping decision making technique based decision support framework for R&D budget allocation in South Korea," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    5. Eslam Mohammed Abdelkader & Tarek Zayed & Hassan El Fathali & Ghasan Alfalah & Abobakr Al-Sakkaf & Osama Moselhi, 2023. "An Integrated Multi-Criteria Decision Making Model for the Assessment of Public Private Partnerships in Transportation Projects," Mathematics, MDPI, vol. 11(16), pages 1-41, August.
    6. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    7. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    8. Kamrani, Kazem & Roozbahani, Abbas & Hashemy Shahdany, Seied Mehdy, 2020. "Using Bayesian networks to evaluate how agricultural water distribution systems handle the water-food-energy nexus," Agricultural Water Management, Elsevier, vol. 239(C).
    9. Atiyeh Bozorgi & Abbas Roozbahani & Seied Mehdy Hashemy Shahdany & Rouzbeh Abbassi, 2021. "Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3139-3159, August.
    10. Vassilios A. Tsihrintzis & Harris Vangelis, 2018. "Water Resources and Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4813-4817, December.
    11. Javad Shafiee Neyestanak & Abbas Roozbahani, 2021. "Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 5049-5072, November.

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