Hydrochemical characterization of surface waters in Northen Tehran: Integrating cluster-based techniques with Self-Organizing Maps

Water quality is a critical global issue, especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems. This study evaluates the hydrochemical characteristics of surface water in the North of Tehran Rivers (NTRs), an essential water resource in a rapidly urbanizing region, using advanced clustering techniques, including Hierarchical Clustering Analysis (HCA), Fuzzy C-Means (FCM), Genetic Algorithm Fuzzy C-Means (GAFCM), and Self-Organizing Map (SOM). The research aims to address the scientific challenge of understanding spatial and temporal variability in water quality, focusing on physicochemical parameters, hydrochemical facies, and contamination sources. Water samples from six rivers collected over four seasons in 2020 were analyzed and classified into distinct clusters based on their chemical composition, revealing significant seasonal and spatial differences. Results showed that FCM and GAFCM consistently categorized the NTRs into two clusters during winter and spring and three in summer and autumn. These findings were supported by HCA and SOM, which identified clusters corresponding to specific river segments and contamination levels. The primary hydrochemical processes identified were mineral dissolution and weathering, with calcite, dolomite, and aragonite significantly influencing water chemistry. Additionally, human activities, such as wastewater discharge, were shown to contribute to elevated sulfate, nitrate, and phosphate concentrations, further corroborated by microbial analyses. By integrating HCA, FCM, and GAFCM with an artificial neural network (ANN)-based clustering method (SOM), this study provides a robust framework for evaluating surface water quality. The findings, supported by Gibbs diagrams, Hounslow ion ratio, and saturation indices, highlight the dominance of rock weathering and human impacts in shaping the hydrochemical dynamics of the NTRs. These insights contribute to the scientific understanding of water quality dynamics and offer practical guidance for sustainable water resource management and environmental protection in developing urban areas.