Arid
DOI10.1007/s11269-017-1786-3
An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers
Triki, Chefi1,2; Zekri, Slim3; Al-Maktoumi, Ali4; Fallahnia, Mahsa5
通讯作者Triki, Chefi
来源期刊WATER RESOURCES MANAGEMENT
ISSN0920-4741
EISSN1573-1650
出版年2017
卷号31期号:15页码:4925-4939
英文摘要

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.


英文关键词Aquifer management Uncertainty Seawater intrusion Groundwater simulation M5P model tree Multi-objective optimization
类型Article
语种英语
国家Oman ; Italy ; Iran
收录类别SCI-E
WOS记录号WOS:000414039900013
WOS关键词GROUNDWATER RESOURCES ; CONJUNCTIVE USE ; MODEL TREE ; M5P MODEL ; OPTIMIZATION ; PREDICTION ; SYSTEMS ; RESERVOIR ; SURFACE
WOS类目Engineering, Civil ; Water Resources
WOS研究方向Engineering ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/202894
作者单位1.Sultan Qaboos Univ, Dept Mech & Ind Engn, Muscat, Oman;
2.Univ Salento, Dept Engn Innovat, Lecce, Italy;
3.Sultan Qaboos Univ, Dept Nat Resource Econ, Muscat, Oman;
4.Sultan Qaboos Univ, Dept Soil Water & Agr Engn, Muscat, Oman;
5.Art Univ, Fac Architecture & Urban Planning, Dept Architecture, Tehran, Iran
推荐引用方式
GB/T 7714
Triki, Chefi,Zekri, Slim,Al-Maktoumi, Ali,et al. An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers[J],2017,31(15):4925-4939.
APA Triki, Chefi,Zekri, Slim,Al-Maktoumi, Ali,&Fallahnia, Mahsa.(2017).An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers.WATER RESOURCES MANAGEMENT,31(15),4925-4939.
MLA Triki, Chefi,et al."An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers".WATER RESOURCES MANAGEMENT 31.15(2017):4925-4939.
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