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

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.

Automated summary

This study proposes a new hybrid framework for assessing the vulnerability of the Al-Khoud coastal aquifer in Oman. The framework combines statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods. The vulnerability index model GALDIT is modified by adding the parameter of bedrock topography (BT) and removing the aquifer type parameter. The GALDIT rates are further adjusted using the frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods, and the weights are optimized with the non-dominated sorting genetic algorithm-II (NSGA-II). The resulting coastal aquifer vulnerability index (CAVI) model highlights areas of high vulnerability in the Northern aquifers and shows a strong correlation (0.78) with total dissolved solids (TDS) as an indicator of groundwater quality.