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.

This paper collected 134 groups of shallow groundwater samples from Luannan County from 2015 to 2023, constructed the groundwater quality evaluation model by the Random Forest method, and used the USEPA model to evaluate human health. The results showed that: (1) the groundwater quality in the study area was generally good, and only the samples of superwater accounted for 10.4%. The areas with poor water quality are concentrated in the southwest and north, and the central and eastern regions are good; (2) the importance evaluation of Random Forest shows that Mn2+ is the most important index affecting groundwater quality, mainly derived from mine drainage and unreasonable use of pesticides and fertilizers; (3) the proportion of children with HItotal greater than 1 reaches 64%, indicating that children are the most vulnerable population. Measures such as controlling pollution sources, regular monitoring, and health risk assessment are recommended to reduce risk.