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dc.citation.startPage 133196 -
dc.citation.title JOURNAL OF HAZARDOUS MATERIALS -
dc.citation.volume 465 -
dc.contributor.author Jeong, Heewon -
dc.contributor.author Park, Sanghyun -
dc.contributor.author Choi, Byeongwook -
dc.contributor.author Yu, Chung Seok -
dc.contributor.author Hong, Ji Young -
dc.contributor.author Jeong, Tae-Yong -
dc.contributor.author Cho, Kyung Hwa -
dc.date.accessioned 2024-02-07T18:05:11Z -
dc.date.available 2024-02-07T18:05:11Z -
dc.date.created 2024-02-01 -
dc.date.issued 2024-03 -
dc.description.abstract Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna. Six ML models were adopted to predict contamination alarms from Daphnia behavioral parameters. The light gradient boosting machine model demonstrated the most significant improvement in predicting alarms from Daphnia behaviors. Compared with the traditional BEWS alarm index, the ML model enhanced the precision and recall by 29.50% and 43.41%, respectively. The speed distribution index and swimming speed were significant parameters for predicting water quality warnings. The nonlinear relationships between the monitored Daphnia behaviors and water physicochemical water quality parameters (i. e., flow rate, Chlorophyll-a concentration, water temperature, and conductivity) were identified by ML models for simulating Daphnia behavior based on the water contaminants. These findings suggest that ML models have the potential to establish a robust framework for advancing the predictive capabilities of BEWS, providing a promising avenue for real-time and accurate assessment of water quality. Thereby, it can contribute to more proactive and effective water quality management strategies. -
dc.identifier.bibliographicCitation JOURNAL OF HAZARDOUS MATERIALS, v.465, pp.133196 -
dc.identifier.doi 10.1016/j.jhazmat.2023.133196 -
dc.identifier.issn 0304-3894 -
dc.identifier.scopusid 2-s2.0-85180419412 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/81331 -
dc.identifier.wosid 001143557900001 -
dc.language 영어 -
dc.publisher ELSEVIER -
dc.title Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Engineering, Environmental; Environmental Sciences -
dc.relation.journalResearchArea Engineering; Environmental Sciences & Ecology -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Biological early warning system -
dc.subject.keywordAuthor Machine learning models -
dc.subject.keywordAuthor Water quality -
dc.subject.keywordAuthor Explainable models -
dc.subject.keywordAuthor Daphnia magna -
dc.subject.keywordPlus INDICATOR BACTERIA -
dc.subject.keywordPlus SWIMMING BEHAVIOR -
dc.subject.keywordPlus TOXICITY -
dc.subject.keywordPlus FILTRATION -

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