The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO₄-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO₄-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management.

South Korea’s National Institute of Environmental Research (NIER) operates an algae alert system to monitor water quality at public water supply source sites. Accurate prediction of dominant harmful cyanobacterial genera, such as Aphanizomenon, Anabaena, Oscillatoria, and Microcystis, is crucial for managing water source contamination risks. This study utilized data collected between January 2017 and December 2022 from Juam Lake and Tamjin Lake, which are representative water supply source sites at the Yeongsan River and Seomjin River basins. We performed an exploratory data analysis on the monitored water quality parameters to understand overall fluctuations. Using data from 2017 to 2021 as training data and 2022 data as test data, we compared the dominant algal classification accuracy of 11 statistical machine learning algorithms. The results indicated that the optimal algorithm varied depending on the survey site and evaluation criteria, highlighting the unique environmental characteristics of each site. By predicting dominant algae in advance, stakeholders can better prepare for water source contamination accidents. Our findings demonstrate the applicability of machine learning algorithms as efficient tools for managing water quality in water supply source systems using monitoring data.