No abstract

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.

No abstract

In this study, we investigated the interrelationships between organic matter and water quality indices in the total maximum daily load basins, namely, Churyeongcheon and Yocheon of the Seomjin River system, and identified trends. Churyeong A and Yocheon B, the basins being analyzed, have high proportions of nonpoint pollution sources and pollutant loads from terrestrial sources. During the study period, biochemical oxygen demand (BOD) decreased in both basins, whereas chemical oxygen demand (COD) and total organic carbon (TOC) increased in Churyeong A and decreased in Yocheon B. The increase in organic matter in Churyeong A correlated with the flow rate, whereas organic matter in Yocheon B showed little correlation with flow rate. Variations in organic matter (BOD, COD, and TOC) in Churyeong A exhibited seasonality under the influence of increased flow rate. Organic matter in Yocheon B was affected by increased flow rate, wherein with time, BOD decreased and COD and TOC increased. This study provides basic data that can be used as a reference to facilitate continuous water management and appropriate strategy implementation by analyzing the influencing factors and trends of organic matter using long-term measurement data.

No abstract