New perspective on density-based spatial clustering of applications with noise for groundwater assessment - Jibrin, Abdulhayat M.; Al-Suwaiyan, Mohammad; Yaseen, Zaher Mundher; Abba, Sani I. (Journal of hydrology (Amsterdam), 2025)
•Novel frameworks were developed for groundwater assessment in arid environments.•Polynomial kernel outperformed other types in reducing the dimensionality of the dataset.•DBSCAN detected groundwater clusters and identified contamination hotspots effectively.•Seawater intrusion and over-extraction were the primary causes of increasing salinity levels. This study introduces an integrated approach combining Kernel Principal Component Analysis (Kernel PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for the assessment of groundwater quality in arid environments. Kernel PCA was employed to reduce the dimensionality of high-dimensional datasets, outlier handling, and enhanced cluster separation. Five kernel types viz: linear, polynomial, radial basis function (RBF), sigmoid, and cosine; were compared, with the polynomial kernel demonstrating superior performance in preserving variance and achieving effective dimensionality reduction. DBSCAN identified spatial clusters and anomalies (outliers) in groundwater quality, with optimal eps = 0.05 and minPts = 3, determined using the Silhouette Score (SS) and Davies-Bouldin Index (DBI). The analysis revealed higher salinity levels influenced by seawater intrusion and over-extraction due to heavily urbanized and agricultural areas. The spatial clustering analysis provides a comprehensive view of distinct physicochemical zones and contamination hotspots. This novel Kernel PCA-DBSCAN framework enhances the detailing of groundwater quality assessment of physicochemical patterns and supports sustainable resource management.