Kernel principal component analysis-based water quality index modelling for coastal aquifers in Saudi Arabia

This study developed a novel Water Quality Index (WQI) using Kernel Principal Component Analysis (PCA) to assess groundwater quality (GWQ) in the coastal aquifers of Al-Qatif, Saudi Arabia. A total of 39 groundwater samples were collected from shallow and deep wells and analyzed for key physicochemical parameters. Six kernel types were tested, and the polynomial kernel was found to be most effective in preserving variance and reducing dimensionality. The Kernel PCA-based WQI classified wells into 'Very Bad,' 'Bad,' and 'Medium' categories, with scores such as W3 (WQI = 25.51, "Very Bad"), W31 (WQI = 46.7, "Bad"), and W38 (WQI = 56.75, "Medium"). Salinity and EC presented poor Sub-Index (SI) scores, reflecting the impact of seawater intrusion and over-extraction, while pH consistently showed high SI values (100), indicating natural buffering. By integrating non-linear dimensionality reduction, the proposed framework enhances traditional WQIs and facilitates more targeted and transparent groundwater decision-making. This includes identifying priority wells for remediation and supporting sustainable abstraction policies. The findings offer insight into sustainable water management in arid and semi-arid regions that are confronting groundwater degradation.