High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things
Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1636 |
| _version_ | 1797476689282859008 |
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| author | Tianying Sun Yuan Feng Chen Li Xingzhi Zhang |
| author_facet | Tianying Sun Yuan Feng Chen Li Xingzhi Zhang |
| author_sort | Tianying Sun |
| collection | DOAJ |
| description | Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena. |
| first_indexed | 2024-03-09T21:05:13Z |
| format | Article |
| id | doaj.art-71c747ce22ad4ac8b8ddfe527f586882 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:05:13Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-71c747ce22ad4ac8b8ddfe527f5868822023-11-23T22:02:44ZengMDPI AGSensors1424-82202022-02-01224163610.3390/s22041636High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of ThingsTianying Sun0Yuan Feng1Chen Li2Xingzhi Zhang3College of Information Science and Engineering, Ocean University of China, Qingdao 266005, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao 266005, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao 266005, ChinaKey Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China, Qingdao 266005, ChinaImpacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.https://www.mdpi.com/1424-8220/22/4/1636sea surface temperaturetemporal convolutional networkIndian OceanInternet of Things |
| spellingShingle | Tianying Sun Yuan Feng Chen Li Xingzhi Zhang High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things Sensors sea surface temperature temporal convolutional network Indian Ocean Internet of Things |
| title | High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things |
| title_full | High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things |
| title_fullStr | High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things |
| title_full_unstemmed | High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things |
| title_short | High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things |
| title_sort | high precision sea surface temperature prediction of long period and large area in the indian ocean based on the temporal convolutional network and internet of things |
| topic | sea surface temperature temporal convolutional network Indian Ocean Internet of Things |
| url | https://www.mdpi.com/1424-8220/22/4/1636 |
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