This study employed hydrochemical data, traditional hydrogeochemical methods, inverse hydrogeochemical modeling, and unsupervised machine learning techniques to explore the hydrogeochemical traits and origins of groundwater in the Changbai Mountain region. (1) Findings reveal that predominant hydrochemical types include HCO₃⁻Ca·Mg, HCO₃⁻Ca·Na·Mg, HCO₃⁻Mg·Na, and HCO₃⁻Na·Mg. The average metasilicic acid content was found to be at 49.13 mg/L. (2) Rock weathering mechanisms, particularly silicate mineral weathering, primarily shape groundwater chemistry, followed by carbonate dissolution. (3) Water-rock interactions involve volcanic mineral dissolution and cation exchange adsorption. Inverse hydrogeochemical modeling, alongside analysis of the widespread volcanic lithology, underscores the complexity of groundwater reactions, influenced not only by water-rock interactions but also by evaporation and precipitation. (4) Unsupervised machine learning, integrating SOM, PCA, and K-means techniques, elucidates hydrochemical types. SOM component maps reveal a close combination of various hydrochemical components. Principal component analysis (PCA) identifies the first principal component (PC1), explaining 48.15% of the variance. The second (PC2) and third (PC3) principal components, explain 13.2% and 10.8% of the variance, respectively. K clustering categorized samples into three main clusters: one less influenced by basaltic geological processes, another showing strong igneous rock weathering characteristics, and the third affected by other geological processes or anthropogenic factors.

Hydrochemical data were utilized in this study to elucidate the hydrogeochemical characteristics and genesis of high-arsenic groundwater in the North China Plain, employing both traditional hydrogeochemical analysis and unsupervised machine learning techniques. The findings indicate that the predominant hydrochemical types of groundwater in the study area are HCO₃-Ca·Na and SO₄·Cl-Na·Ca. The primary mechanism influencing groundwater chemistry has been identified as rock weathering. The unsupervised machine learning framework incorporates various methods, such as principal component analysis (PCA), non-negative matrix factorization (NMF), machine learning models (gradient boosting trees and random forests), and cluster analysis to explore the characteristics and genesis of groundwater hydrochemical types within the study area. This study demonstrated that the formation mechanism of high-arsenic groundwater results from multiple interacting factors.

This study investigated the hydrochemical characteristics, solute sources, and controlling factors of the Qujiang River, a vital tributary of the Qiantangjiang River, on the basis of 61 surface water samples collected from July to September 2019. A multi-method framework integrating ArcGIS spatial analysis, a Piper trilinear diagram, a Gibbs diagram, ion ratio analysis, and principal component analysis (PCA) was systematically applied. The results demonstrate significant spatial heterogeneity in pH values (6.39–7.15 in the upper reaches, 6.31–8.83 in the middle reaches, and 6.85–8.1 in the lower reaches), with hydrochemical facies dominated by Ca-HCO₃ types (87% frequency) in the upper–middle reaches, transitioning to SO₄·Cl-Na and SO₄·Cl-Na·Ca mixed types downstream. Source apportionment indicates that carbonate weathering and atmospheric precipitation are the primary controls on hydrochemistry in the upper and middle reaches, whereas industrial effluents, evidenced by a 2.3-fold increase in SO₄²⁻ concentration, and domestic sewage, with Cl⁻ levels reaching 16.07 mg/L, are the dominant influences in the lower reaches. This study’s innovation lies in the quantitative separation of natural weathering (68% contribution) and anthropogenic activities (32%) through an integrated methodological approach, offering a basin-scale understanding of longitudinal hydrochemical evolution. These results provide valuable insights for the management and ecological conservation of medium- to small-sized basins.