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An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

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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.

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Acknowledgements

This research was partially supported by internal grant IG/ENG/MIED/14/01 from Sultan Qaboos University and by TRC grant number RC/AGR/ECON/12/01. This financial support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Muscat, Oman

    Chefi Triki

  2. Department of Engineering for Innovation, University of Salento, Lecce, Italy

    Chefi Triki

  3. Department of Natural Resource Economics, Sultan Qaboos University, Muscat, Oman

    Slim Zekri

  4. Department of Soil, Water and Agriculture Engineering, Sultan Qaboos University, Muscat, Oman

    Ali Al-Maktoumi

  5. Department of Architecture, Faculty of Architecture and Urban Planning, Art University, Tehran, Iran

    Mahsa Fallahnia

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  1. Chefi Triki
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  2. Slim Zekri
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  3. Ali Al-Maktoumi
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  4. Mahsa Fallahnia
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Correspondence to Chefi Triki.

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Triki, C., Zekri, S., Al-Maktoumi, A. et al. An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers. Water Resour Manage 31, 4925–4939 (2017). https://doi.org/10.1007/s11269-017-1786-3

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  • DOI: https://doi.org/10.1007/s11269-017-1786-3

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