A Decision Support Tool for the Dynamic Groundwater Management of Coastal Aquifers Under Uncertainty

Chefi Triki*, Mohammad Mahdi Rajabi, Ali Al-Maktoumi, Slim Zekri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we described a computationally efficient simulation–optimization (S/O) framework for coastal groundwater management (CGM), based on the combined application of numerical modeling, artificial neural networks, and genetic algorithm. The objective was to analyze the ‘trade-off’ between optimality and risk in deriving CGM strategies and to show that reformulating the problem using a mean–variance bi-criterion objective function can be a valuable tool in minimizing the risk of non-optimality. As a case study for our analysis, we studied the optimal design of an aquifer storage and recovery (ASR) system in the Muscat metropolitan area in Oman. S/O was applied to find the optimal constant daily abstraction and injection rates that maximize the net present value (NPV) of the ASR system. The results show that the choice of the decision variables significantly affects the risk of non-optimality, and reducing this risk comes at the cost of a decrease in the expected NPV.

Original languageEnglish
Title of host publicationSelected Studies in Environmental Geosciences and Hydrogeosciences - Proceedings of the 3rd Conference of the Arabian Journal of Geosciences CAJG-3
EditorsAmjad Kallel, Maurizio Barbieri, Jesús Rodrigo-Comino, Helder I. Chaminé, Broder Merkel, Haroun Chenchouni, Jasper Knight, Sandeep Panda, Nabil Khélifi, Ali Cemal Benim, Stefan Grab, Hesham El-Askary, Santanu Banerjee, Riheb Hadji, Mehdi Eshagh
PublisherSpringer Nature
Pages241-243
Number of pages3
ISBN (Print)9783031438028
DOIs
Publication statusPublished - 2023
Event3rd Springer Conference of the Arabian Journal of Geosciences, CAJG-3 2020 - Virtual, Online
Duration: Nov 2 2020Nov 5 2020

Publication series

NameAdvances in Science, Technology and Innovation
ISSN (Print)2522-8714
ISSN (Electronic)2522-8722

Conference

Conference3rd Springer Conference of the Arabian Journal of Geosciences, CAJG-3 2020
CityVirtual, Online
Period11/2/2011/5/20

Keywords

  • Artificial neural network
  • Coastal aquifer
  • Robust optimization
  • Uncertainty quantification

ASJC Scopus subject areas

  • Architecture
  • Environmental Chemistry
  • Renewable Energy, Sustainability and the Environment

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