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Developing a decision-making model for improving the groundwater balance to control land subsidence

Author

Listed:
  • Mojtaba Zangeneh

    (Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)

  • Mahdi Sarai Tabrizi

    (Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)

  • Amir Khosrojerdi

    (Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)

  • Ali Saremi

    (Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)

Abstract

This study aimed to control land subsidence by improving the groundwater balance in the Varamin plain using the Groundwater Modelling System software and a multi-criteria decision model. For this purpose, aquifer level quantification and subsidence rate simulation were performed with the MODFLOW model and SUB package, respectively. The results showed a 6 m decrease in the aquifer level over a 5-year period and the subsidence rate in the central parts was 37 cm. Accordingly, the aquifer was evaluated by considering eight different restoration strategies based on reduced exploitation and artificial feeding. The results showed that the environmental criterion related to the subsidence adjustment index had the highest weight (0.27) and was introduced as the most important decision-making criterion. The evaluation of the results and priorities using the Complex Proportional Assessment (COPRAS) method showed that a 30% reduction in exploitation with artificial feeding is the best restoration strategy and can improve the subsidence rate and aquifer level by 36% and 76%, respectively, over a 5-year period (2024).

Suggested Citation

  • Mojtaba Zangeneh & Mahdi Sarai Tabrizi & Amir Khosrojerdi & Ali Saremi, 2023. "Developing a decision-making model for improving the groundwater balance to control land subsidence," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(1), pages 55-65.
    • Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:57-2022-swr
    DOI: 10.17221/57/2022-SWR
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    References listed on IDEAS

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    1. 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.
    2. Ghenai, Chaouki & Albawab, Mona & Bettayeb, Maamar, 2020. "Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method," Renewable Energy, Elsevier, vol. 146(C), pages 580-597.
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