Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model
Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3654 |
| _version_ | 1797548571649638400 |
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| author | Minkyu Kim Hyun Yang Jonghwa Kim |
| author_facet | Minkyu Kim Hyun Yang Jonghwa Kim |
| author_sort | Minkyu Kim |
| collection | DOAJ |
| description | Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry. |
| first_indexed | 2024-03-10T15:01:27Z |
| format | Article |
| id | doaj.art-c420a57f651b4e1da898d651f3739c83 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:01:27Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c420a57f651b4e1da898d651f3739c832023-11-20T20:09:24ZengMDPI AGRemote Sensing2072-42922020-11-011221365410.3390/rs12213654Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory ModelMinkyu Kim0Hyun Yang1Jonghwa Kim2School of Ocean Science and Technology, Korea Maritime and Ocean University, Busan 49112, KoreaSchool of Ocean Science and Technology, Korea Maritime and Ocean University, Busan 49112, KoreaSchool of Ocean Science and Technology, Korea Maritime and Ocean University, Busan 49112, KoreaRecent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry.https://www.mdpi.com/2072-4292/12/21/3654high water temperatureHWTlong short-term memoryLSTMrecurrent neural networkRNN |
| spellingShingle | Minkyu Kim Hyun Yang Jonghwa Kim Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model Remote Sensing high water temperature HWT long short-term memory LSTM recurrent neural network RNN |
| title | Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model |
| title_full | Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model |
| title_fullStr | Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model |
| title_full_unstemmed | Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model |
| title_short | Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model |
| title_sort | sea surface temperature and high water temperature occurrence prediction using a long short term memory model |
| topic | high water temperature HWT long short-term memory LSTM recurrent neural network RNN |
| url | https://www.mdpi.com/2072-4292/12/21/3654 |
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