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An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Author

Listed:
  • Chefi Triki

    (Sultan Qaboos University
    University of Salento)

  • Slim Zekri

    (Sultan Qaboos University)

  • Ali Al-Maktoumi

    (Sultan Qaboos University)

  • Mahsa Fallahnia

    (Art University)

Abstract

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.

Suggested Citation

  • Chefi Triki & Slim Zekri & Ali Al-Maktoumi & Mahsa Fallahnia, 2017. "An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4925-4939, December.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:15:d:10.1007_s11269-017-1786-3
    DOI: 10.1007/s11269-017-1786-3
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    References listed on IDEAS

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    1. Akbar Javadi & Mohammed Hussain & Mohsen Sherif & Raziyeh Farmani, 2015. "Multi-objective Optimization of Different Management Scenarios to Control Seawater Intrusion in Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1843-1857, April.
    2. George Kourakos & Aristotelis Mantoglou, 2011. "Simulation and Multi-Objective Management of Coastal Aquifers in Semi-Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1063-1074, March.
    3. Mohammad Nikoo & Akbar Karimi & Reza Kerachian & Hamed Poorsepahy-Samian & Farhang Daneshmand, 2013. "Rules for Optimal Operation of Reservoir-River-Groundwater Systems Considering Water Quality Targets: Application of M5P Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2771-2784, June.
    4. Slim Zekri & Chefi Triki & Ali Al-Maktoumi & Mohammad Bazargan-Lari, 2015. "An Optimization-Simulation Approach for Groundwater Abstraction under Recharge Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3681-3695, August.
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