Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models

Mojgan Bordbar, Mohammad Reza Nikoo*, Ahmad Sana, Banafsheh Nematollahi, Ghazi Al-Rawas, Amir H. Gandomi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.

Original languageEnglish
JournalHydrological Sciences Journal
Publication statusAccepted/In press - 2023


  • coastal aquifer vulnerability index (CAVI)
  • frequency ratio (FR)
  • non-dominated sorting genetic algorithm-II (NSGA-II)
  • random forest (RF)
  • stepwise weight assessment ratio analysis (SWARA)

ASJC Scopus subject areas

  • Water Science and Technology

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