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<p>Characterizing regional groundwater chemistry and quality is essential for sustainable water resource management, yet remains challenging due to spatial complexity arising from both natural and anthropogenic factors. In this study, a hybrid Self-Organizing Map (SOM) and Principal Component Analysis (PCA) approach was applied, followed by Hierarchical Cluster Analysis (HCA), to interpret the hydrochemical characteristics of groundwater in the Qorveh–Dehgolan basin, Iran. A total of 112 groundwater samples collected during dry and wet seasons were analyzed. To ensure optimal performance, multiple SOM map sizes and normalization techniques (Z-score, Min–Max, and log(1 + x)) were tested and evaluated using Quantization Error (QE), Topographic Error (TE), and Explained Variance (EV). The 8 × 7 SOM grid (56 neurons) was selected as the final configuration, as it produced the lowest QE and TE and the highest EV. The optimized SOM results were subsequently grouped into four clusters based on the combined evaluation of SOM and HCA outcomes. Hydrogeochemical processes were interpreted using Piper and Gibbs diagrams, as well as cation exchange indices. Results indicated a dominant Ca<sup>2</sup>⁺–HCO<sub>3</sub>⁻ water type across all clusters (1–4). Cation concentrations followed the order Ca<sup>2</sup>⁺ &gt; Mg<sup>2</sup>⁺ &gt; Na⁺ + K⁺, while the dominant anion sequence was HCO<sub>3</sub>⁻ &gt; Cl⁻ &gt; SO<sub>4</sub><sup>2</sup>⁻. Ionic ratio analyses revealed that elevated NO<sub>3</sub>⁻ concentrations are largely attributable to agricultural fertilizer use and domestic wastewater infiltration, highlighting anthropogenic impacts on groundwater quality. In contrast, natural geochemical processes, including silicate weathering and carbonate dissolution, were identified as the predominant mechanisms controlling groundwater evolution. Overall, the integrated SOM–PCA–HCA framework effectively captured both natural and human-induced variability in groundwater chemistry, and distinguished seasonal variations in water quality, underscoring its applicability for sustainable groundwater management in complex aquifer systems.</p>

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Groundwater hydrochemistry interpretation based on self-organizing maps and hierarchical clustering in Qorveh-Dehgolan plain, Western Iran

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Narges Bayat,
  • Hamid Reza Nassery,
  • Erfan Sadeghi

摘要

Characterizing regional groundwater chemistry and quality is essential for sustainable water resource management, yet remains challenging due to spatial complexity arising from both natural and anthropogenic factors. In this study, a hybrid Self-Organizing Map (SOM) and Principal Component Analysis (PCA) approach was applied, followed by Hierarchical Cluster Analysis (HCA), to interpret the hydrochemical characteristics of groundwater in the Qorveh–Dehgolan basin, Iran. A total of 112 groundwater samples collected during dry and wet seasons were analyzed. To ensure optimal performance, multiple SOM map sizes and normalization techniques (Z-score, Min–Max, and log(1 + x)) were tested and evaluated using Quantization Error (QE), Topographic Error (TE), and Explained Variance (EV). The 8 × 7 SOM grid (56 neurons) was selected as the final configuration, as it produced the lowest QE and TE and the highest EV. The optimized SOM results were subsequently grouped into four clusters based on the combined evaluation of SOM and HCA outcomes. Hydrogeochemical processes were interpreted using Piper and Gibbs diagrams, as well as cation exchange indices. Results indicated a dominant Ca2⁺–HCO3⁻ water type across all clusters (1–4). Cation concentrations followed the order Ca2⁺ > Mg2⁺ > Na⁺ + K⁺, while the dominant anion sequence was HCO3⁻ > Cl⁻ > SO42⁻. Ionic ratio analyses revealed that elevated NO3⁻ concentrations are largely attributable to agricultural fertilizer use and domestic wastewater infiltration, highlighting anthropogenic impacts on groundwater quality. In contrast, natural geochemical processes, including silicate weathering and carbonate dissolution, were identified as the predominant mechanisms controlling groundwater evolution. Overall, the integrated SOM–PCA–HCA framework effectively captured both natural and human-induced variability in groundwater chemistry, and distinguished seasonal variations in water quality, underscoring its applicability for sustainable groundwater management in complex aquifer systems.