Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causi...
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| author | Tianliang Xu Zhiquan Zhou Chenxu Wang Yingchun Li Tian Rong |
| author_facet | Tianliang Xu Zhiquan Zhou Chenxu Wang Yingchun Li Tian Rong |
| author_sort | Tianliang Xu |
| collection | DOAJ |
| description | A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years. |
| format | Article |
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| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-3e93ef0818644db788dee182d045adcf2025-08-20T02:17:59ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113475510.3390/jmse13040755Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion NetworkTianliang Xu0Zhiquan Zhou1Chenxu Wang2Yingchun Li3Tian Rong4School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaA basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years.https://www.mdpi.com/2077-1312/13/4/755equatorial Pacific OceanEl Niñosea surface temperature (SST)convolutional long short-term memory (ConvLSTM) |
| spellingShingle | Tianliang Xu Zhiquan Zhou Chenxu Wang Yingchun Li Tian Rong Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network Journal of Marine Science and Engineering equatorial Pacific Ocean El Niño sea surface temperature (SST) convolutional long short-term memory (ConvLSTM) |
| title | Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network |
| title_full | Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network |
| title_fullStr | Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network |
| title_full_unstemmed | Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network |
| title_short | Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network |
| title_sort | spatio temporal prediction of surface remote sensing data in equatorial pacific ocean based on multi element fusion network |
| topic | equatorial Pacific Ocean El Niño sea surface temperature (SST) convolutional long short-term memory (ConvLSTM) |
| url | https://www.mdpi.com/2077-1312/13/4/755 |
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