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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1093 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1077 | - |
dc.citation.title | Korean Journal of Remote Sensing | - |
dc.citation.volume | 36 | - |
dc.contributor.author | Jung, Sihun | - |
dc.contributor.author | Kim, Young Jun | - |
dc.contributor.author | Park, Sumin | - |
dc.contributor.author | Im, Jungho | - |
dc.date.accessioned | 2023-12-21T16:47:17Z | - |
dc.date.available | 2023-12-21T16:47:17Z | - |
dc.date.created | 2021-01-08 | - |
dc.date.issued | 2020-10 | - |
dc.description.abstract | Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance. | - |
dc.identifier.bibliographicCitation | Korean Journal of Remote Sensing, v.36, no.5, pp.1077 - 1093 | - |
dc.identifier.doi | 10.7780/kjrs.2020.36.5.3.7 | - |
dc.identifier.issn | 1225-6161 | - |
dc.identifier.scopusid | 2-s2.0-85106461314 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/49503 | - |
dc.language | 한국어 | - |
dc.publisher | 대한원격탐사학회 | - |
dc.title.alternative | 시계열 기계학습을 이용한 한반도 남해 해수면 온도 예측 및 고수온 탐지 | - |
dc.title | Prediction of sea surface temperature and detection of ocean heat wave in the South sea of Korea using time-series deep-learning approaches | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.identifier.kciid | ART002643763 | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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