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김윤호

Kim, Yunho
Mathematical Imaging Analysis Lab.
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dc.citation.endPage 29003 -
dc.citation.startPage 28988 -
dc.citation.title IEEE ACCESS -
dc.citation.volume 12 -
dc.contributor.author Putri, Tafia Hasna -
dc.contributor.author Caraka, Rezzy Eko -
dc.contributor.author Toharudin, Toni -
dc.contributor.author Kim, Yunho -
dc.contributor.author Chen, Rung-Ching -
dc.contributor.author Gio, Prana Ugiana -
dc.contributor.author Sakti, Anjar Dimara -
dc.contributor.author Pontoh, Resa Septiani -
dc.contributor.author Pratiwi, Indah Reski -
dc.contributor.author Nugraha, Farid Azhar Lutfi -
dc.contributor.author Azzahra, Thalita Safa -
dc.contributor.author Cerelia, Jessica Jesslyn -
dc.contributor.author Darmawan, Gumgum -
dc.contributor.author Faidah, Defi Yusti -
dc.contributor.author Pardamean, Bens -
dc.date.accessioned 2024-04-01T15:35:09Z -
dc.date.available 2024-04-01T15:35:09Z -
dc.date.created 2024-03-29 -
dc.date.issued 2024-03 -
dc.description.abstract Particulate matter forecasting is fundamental for early warning and controlling air pollution, especially PM2.5. The increase in this level of concentration will lead to a negative impact on public health. This study develops a hybrid model of CNN-LSTM and CONV-LSTM by combining a convolutional neural network (CNN) with an LSTM network to forecast PM2.5 concentration for the next few hours in Kemayoran DKI Jakarta, which is known as a busy area. We discovered the advantages of CNN in effectively extracting features and LSTM in learning long-term historical data from PM2.5 concentration time series data. The predictive model of CNN-LSTM is carried out in a different architecture where the CNN process is carried out first to become the input of LSTM. For CONV-LSTM, it is carried out in one architecture where the multiplication in the LSTM architecture is coupled with the convolution process. This research will explain how the method of developing hybrid CNN-LSTM and CONV-LSTM in predicting PM2.5 concentrations. Based on metric evaluation, the two models are compared to find the best model. Both predictive models produce MAPE values that fall into the good enough category with values < 20%. Results were obtained for CONV-LSTM with MAE worth 6.52, RMSE 8.55, and MAPE 16.39%. As a result, the CONV-LSTM model performs better than CNN-LSTM in nowcasting PM2.5. -
dc.identifier.bibliographicCitation IEEE ACCESS, v.12, pp.28988 - 29003 -
dc.identifier.doi 10.1109/ACCESS.2024.3368034 -
dc.identifier.issn 2169-3536 -
dc.identifier.scopusid 2-s2.0-85186106946 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/81927 -
dc.identifier.wosid 001173214700001 -
dc.language 영어 -
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC -
dc.title Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications -
dc.relation.journalResearchArea Computer Science; Engineering; Telecommunications -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor PM2.5 -
dc.subject.keywordAuthor time series -
dc.subject.keywordAuthor CNN -
dc.subject.keywordAuthor LSTM -
dc.subject.keywordAuthor nowcasting -
dc.subject.keywordPlus SHORT-TERM-MEMORY -
dc.subject.keywordPlus NEURAL-NETWORK -
dc.subject.keywordPlus AIR-QUALITY -
dc.subject.keywordPlus CLASSIFICATION -
dc.subject.keywordPlus REGRESSION -
dc.subject.keywordPlus FRAMEWORK -
dc.subject.keywordPlus FORECAST -

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