Tracing Source and Concentration of Riverine Organic Carbon Transporting from Tamjin River to Gangjin Bay, Korea

Park, Hyung-Geun; Kang, Dong-Won; Shin, Kyung-Hoon; Ock, Giyoung

doi:10.11614/ksl.2017.50.4.422

Cited by 5 publications

(2 citation statements)

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“…Juam Dam supplies about 640 × 10 3 tons of potable wate to the western part of Jeollanam-do, including Gwangju, Naju, Mokpo, and Hwasun [13 Tamjin Lake is an artificial lake created by the construction of Jangheung Dam, which ha a height of 53 m and a length of 403 m. It has a total basin area of 193 km 2 and a total wate storage capacity of 191 × 10 6 tons. It is located in Yuchi-myeon, Jangheung-gun, Jeollanam do, and supplies 73 × 10 6 tons of potable water to 9 cities in Jeollanam-do [14]. Figure shows the sampling sites at Juam Lake and Tamjin Lake.…”

Section: Study Areamentioning

confidence: 99%

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Evaluating Statistical Machine Learning Algorithms for Classifying Dominant Algae in Juam Lake and Tamjin Lake, Republic of Korea

Hwang

Choi

Park

et al. 2023

Water

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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.

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Section: Study Areamentioning

confidence: 99%

“…14 26.74 E/ 35 • 03 23.78 N) and Shinpyeong Bridge (J2, 127 • 13 59.11 E/35 • 00 50.37 N) at Juam Lake, and the dam front (T1, 126 • 52 52.01 E/34 • 45 07.09 N) and Yuchi stream confluence (T2,…”

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confidence: 99%