Predicting Dominance of Toxic-Producing Cyanobacteria Genus by Machine Learning in a Eutrophic River in South Korea

Climate crisis is aggravating harmful algal blooms globally. Especially, cyanobacteria harmful algal blooms (CBs) produce taste and odorous compounds and cyanotoxins (microcystins), thereby threatens human health and ecosystem. Cyanotoxins are known to be produced by mainly Microcystis, Anabaena, Os...

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Bibliographic Details
Published in2023 International Conference on Sustainable Technology and Engineering (i-COSTE) pp. 1 - 5
Main Authors Kim, Jayun, Jung, Woosik, Yeon, Seungjae, An, Jusuk, Oh, Hyunje, Park, Joonhong
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.12.2023
Subjects
algae
Biological system modeling
Compounds
cyanobacteria
Ecosystems
eutrophic river
Graphical models
Machine learning
Nitrogen
nutrients
Rivers
water quality
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Summary:Climate crisis is aggravating harmful algal blooms globally. Especially, cyanobacteria harmful algal blooms (CBs) produce taste and odorous compounds and cyanotoxins (microcystins), thereby threatens human health and ecosystem. Cyanotoxins are known to be produced by mainly Microcystis, Anabaena, Oscillatoria, and Aphanizomenon, In the Republic of Korea, extreme CBs occur at the Nakdong River every summer. This study examined spatial distribution of cyanobacterial composition, characterizes major environmental factors influencing microcystins by using machine learning models in the Nakdong River. It was shown that only Microcystis was significant for microcystin and higher ratio of nitrogen to phosphorous contributes to lower microcystin.
DOI:10.1109/i-COSTE60462.2023.10500770