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Performance Assessment of Two-stream Convolutional Long- and Short-term Memory Model for September Arctic Sea Ice Prediction from 2001 to 2021

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dc.contributor.authorChi, Junhwa-
dc.date.accessioned2023-01-09T16:37:09Z-
dc.date.available2023-01-09T16:37:09Z-
dc.date.issued2022-
dc.identifier.urihttps://repository.kopri.re.kr/handle/201206/14241-
dc.description.abstractSea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional longand short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TSConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5?50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TSConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.-
dc.languageKorean-
dc.subject.classification해당사항없음-
dc.titlePerformance Assessment of Two-stream Convolutional Long- and Short-term Memory Model for September Arctic Sea Ice Prediction from 2001 to 2021-
dc.title.alternativeTwo-stream Convolutional Long- and Short-term Memory 모델의 2001-2021년 9월 북극 해빙 예측 성능 평가-
dc.typeArticle-
dc.identifier.bibliographicCitationChi, Junhwa. 2022. "Performance Assessment of Two-stream Convolutional Long- and Short-term Memory Model for September Arctic Sea Ice Prediction from 2001 to 2021". <em>대한원격탐사학회지</em>, 38(6): 1047-1056.-
dc.citation.title대한원격탐사학회지-
dc.citation.volume38-
dc.citation.number6-
dc.identifier.doi10.7780/kjrs.2022.38.6.1.6-
dc.citation.startPage1047-
dc.citation.endPage1056-
dc.description.articleClassificationKCI등재-
dc.description.jcrRateJCR 2020:0-
dc.subject.keywordArctic-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordCNN-
dc.subject.keywordConvLSTM-
dc.subject.keywordLSTM-
dc.subject.keywordPrediction-
dc.subject.keywordSea ice-
dc.identifier.localId2022-0349-
Appears in Collections  
2022-2022, Preliminary research on Arctic sea ice prediction using artificial intelligence for the extension of Arctic research (22-22) / Chi, Junhwa (PE22420)
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