Integrated clustering analysis for delineating seawater intrusion and heavy metals in Arabian Gulf Coastal groundwater of Saudi Arabia

Abstract

The intrusion of seawater (SWI) into coastal aquifers is a major concern worldwide, affecting the quantity and quality of groundwater resources. The region of Saudi Arabia that lies along the eastern coast has been affected by SWI, making it crucial to accurately identify and monitor the affected areas. This investigation aimed to map the degree of seawater intrusion in a complex aquifer system in the study area using an integrated clustering analysis approach. The study collected 41 groundwater samples from wells penetrating multi-layered aquifers, and the samples were analyzed for physicochemical properties and major ions. Clustering analysis methods, including Hierarchical Clustering Analysis (double-clustering) (HCA-DC), K-mean (KMC), and fuzzy k-mean clustering (FKM), were employed to evaluate the spatial distribution and association of the groundwater properties. The results revealed that the analyzed GW samples were divided into four clusters with varying degrees of SWI. Clusters A, B, C, and D contained GW samples with very low (fsea of 1.9%), high (fsea of 14.9%), intermediate (fsea of 7.9%), and low (fsea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability.