A new approach for assessing the assembled vulnerability of coastal aquifers based on optimization models

Developing Assembled Vulnerability (AV) maps is essential for preventing environmental issues corresponding to the groundwater conditions in aquifers that are exposed to multiple contaminants from multiple origins. There is a research gap in the application of multiple models to produce the AV map of aquifers with two or more origins of contaminant. This study fills this knowledge gap by considering two objectives using the non-dominated sorting genetic algorithm-II (NSGA-II) multi-objective optimization approach to evaluate the vulnerability of coastal urban aquifers to wastewater and seawater intrusion contamination for the first time in the literature. For this purpose, the DRASTICA-GA and GALDIT-GA groundwater vulnerability models as individual models have been combined using NSGA-II to produce the AV maps as multiple models. In this step, two states are considered to produce the AV map: (1) The NSGA-II multi-objective optimization model aims to maximize the correlation coefficients between nitrate (NO₃) and AV as well as total dissolved solids (TDS) and AV, and (2) A qualitative index obtained by data fusion (DF) is used in the NSGA-II model to produce the AV map. Finally, to prove the efficiency and applicability of the proposed framework, it is applied to an important aquifer in Oman, the Al Khoudh aquifer, which shows that the north of the Al Khoudh aquifer is more vulnerable than other parts exposed to wastewater and seawater intrusion contamination. In addition, results indicate that the presented methodology can accurately define the AV indices to identify the vulnerable areas corresponding to the different contamination origins. Moreover, the ROC/AUC criteria for the AV-NSGA-II-DF are more than the AV-NSGA-II, which are 0.962 and 0.956, respectively.