Meta-heuristic algorithms in optimizing GALDIT framework: a comparative study for coastal aquifer vulnerability assessment

Abstract Creating a reliable groundwater vulnerability map is a key solution for protecting the groundwater resources and further planning in coastal aquifers. The GALDIT framework, which is an abbreviation for six parameters of Groundwater occurrence, Aquifer hydraulic conductivity, Level of groundwater above sea level, Distance from the shoreline, Impact of the existing status of seawater intrusion, and Thickness of the aquifer, is a well-known framework for evaluating the groundwater vulnerability in coastal zones. In this study, two meta-heuristic algorithms of Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were proposed to optimize the weights of GALDIT framework. This framework was tested in the Gharesoo-Gorgan Rood coastal aquifer located in the north of Iran. The GALDIT index illustrated poor evaluation of vulnerability to seawater intrusion. Contrariwise, GALDIT-GWO and GALDIT-GA frameworks provided results that are more reasonable. Both vulnerability maps showed a close similarity in terms of vulnerability to seawater intrusion. The vulnerability maps demonstrated that the west and northwest parts of the study area suffer from seawater intrusion. Additionally, the values of Spearman’s rank correlation coefficient between the indices of GALDIT, GALDIT-GWO and GALDIT-GA and the parameter of Cl/HCO3 were obtained as 0.31, 0.53, and 0.49 and the corresponding values for the parameter of TDS were obtained 0.45, 0.64 and 0.60, respectively. Therefore, it can be concluded that the proposed optimization models are able to provide accurate results. Furthermore, these models reduce the subjectivity and increase the capability of the GALDIT index.