Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

물 산업 분야에서의 머신러닝 적용 사례 연구

Full metadata record
DC Field Value Language
dc.contributor.author오원기-
dc.contributor.author장해남-
dc.contributor.author신승구-
dc.date.accessioned2022-12-26T09:21:13Z-
dc.date.available2022-12-26T09:21:13Z-
dc.date.issued2022-02-
dc.identifier.issn1225-7672-
dc.identifier.issn2287-822X-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/2527-
dc.description.abstractWith the current trend of the fourth industrial revolution, machine learning technique is increasingly adopted in various water industry fields. In this review paper, recent studies using machine learning to predict flood, water consumption, water quality, and water treatment processes are summarized. In the typical water purification processes such as flocculation, disinfection, and filtration, machine learning was able to present high-accuracy prediction results for complex non-linear mechanisms. Hybrid machine learning methods, combining multiple algorithms, generally outperformed machine learning results using only one algorithm. A more microscopic machine learning approach can provide valuable information to the operators in the water industry.-
dc.format.extent13-
dc.language한국어-
dc.language.isoKOR-
dc.publisher대한상하수도학회-
dc.title물 산업 분야에서의 머신러닝 적용 사례 연구-
dc.title.alternativeApplication of machine learning in water industry: A review-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.11001/jksww.2022.36.1.9-
dc.identifier.bibliographicCitation상하수도학회지, v.36, no.1, pp 9 - 21-
dc.citation.title상하수도학회지-
dc.citation.volume36-
dc.citation.number1-
dc.citation.startPage9-
dc.citation.endPage21-
dc.identifier.kciidART002810952-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorWater industry-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorArtificial neural network-
dc.subject.keywordAuthorHybrid machine learning-
dc.subject.keywordAuthor물 산업-
dc.subject.keywordAuthor인공지능-
dc.subject.keywordAuthor머신러닝-
dc.subject.keywordAuthor인공신경망-
dc.subject.keywordAuthor하이브리드 머신러닝-
Files in This Item
There are no files associated with this item.
Appears in
Collections
융합기술공과대학 > Department of Energy Engineering > Journal Articles
공과대학 > ETC > Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Shin, Seung Gu photo

Shin, Seung Gu
공과대학 (에너지공학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE