Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method
Ocean temperature is a vital physical variable of the oceans. Accurately predicting the long lead dynamics of the three-dimensional ocean temperature (3D-OT) can help us identify in advance potential extreme events (e.g., droughts and floods) that may be caused by the changes of the 3D-OT, which how...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222001649 |
| _version_ | 1818516479758303232 |
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| author | Changjiang Xiao Xiaohua Tong Dandan Li Xiaojian Chen Qiquan Yang Xiong Xv Hui Lin Min Huang |
| author_facet | Changjiang Xiao Xiaohua Tong Dandan Li Xiaojian Chen Qiquan Yang Xiong Xv Hui Lin Min Huang |
| author_sort | Changjiang Xiao |
| collection | DOAJ |
| description | Ocean temperature is a vital physical variable of the oceans. Accurately predicting the long lead dynamics of the three-dimensional ocean temperature (3D-OT) can help us identify in advance potential extreme events (e.g., droughts and floods) that may be caused by the changes of the 3D-OT, which however remains a challenge. To achieve this goal, a deep learning (DL) model was proposed to make predictions of the monthly 3D-OT for one year ahead using time series gridded Argo data. The DL model is comprised of a one-dimensional convolution (Conv1D) layer which is used for extracting latent features from the time series ocean temperature data, two long short-term memory (LSTM) layers which are used for capturing the long-term temporal dependencies hidden in the 3D-OT based on the features extracted by the Conv1D layer, and a fully-connected layer to output the predictions. The proposed DL model can well model the temporal dependencies and dynamic patterns of the ocean temperature at different spatial locations and in different depths by learning from simply the historical time series gridded Argo data. Experiments conducted in a sub-area of the South Pacific Ocean that predict the monthly 3D-OT with the lead time from 1 to 12 months show that the developed DL model surpasses the persistence model, the AdaBoost model, and the feedforward backpropagation neural network model (BPNN) when compared from multiple spatiotemporal perspectives using multiple statistics, indicating that the proposed DL model is a highly strong model for long lead monthly 3D-OT predictions. |
| first_indexed | 2024-12-11T00:42:50Z |
| format | Article |
| id | doaj.art-1f6c786d0e684d36baf40b0467fb687d |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-11T00:42:50Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-1f6c786d0e684d36baf40b0467fb687d2022-12-22T01:26:52ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102971Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning methodChangjiang Xiao0Xiaohua Tong1Dandan Li2Xiaojian Chen3Qiquan Yang4Xiong Xv5Hui Lin6Min Huang7College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China; Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China; Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaSchool of Software Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, ChinaSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, ChinaCollege of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China; Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaSchool of Geography and Environment, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang 330022, ChinaSchool of Geography and Environment, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang 330022, China; Corresponding author.Ocean temperature is a vital physical variable of the oceans. Accurately predicting the long lead dynamics of the three-dimensional ocean temperature (3D-OT) can help us identify in advance potential extreme events (e.g., droughts and floods) that may be caused by the changes of the 3D-OT, which however remains a challenge. To achieve this goal, a deep learning (DL) model was proposed to make predictions of the monthly 3D-OT for one year ahead using time series gridded Argo data. The DL model is comprised of a one-dimensional convolution (Conv1D) layer which is used for extracting latent features from the time series ocean temperature data, two long short-term memory (LSTM) layers which are used for capturing the long-term temporal dependencies hidden in the 3D-OT based on the features extracted by the Conv1D layer, and a fully-connected layer to output the predictions. The proposed DL model can well model the temporal dependencies and dynamic patterns of the ocean temperature at different spatial locations and in different depths by learning from simply the historical time series gridded Argo data. Experiments conducted in a sub-area of the South Pacific Ocean that predict the monthly 3D-OT with the lead time from 1 to 12 months show that the developed DL model surpasses the persistence model, the AdaBoost model, and the feedforward backpropagation neural network model (BPNN) when compared from multiple spatiotemporal perspectives using multiple statistics, indicating that the proposed DL model is a highly strong model for long lead monthly 3D-OT predictions.http://www.sciencedirect.com/science/article/pii/S1569843222001649One-dimensional convolution (Conv1D)Long short-term memory (LSTM)Time series gridded Argo dataThree-dimensional ocean temperature (3D-OT) prediction |
| spellingShingle | Changjiang Xiao Xiaohua Tong Dandan Li Xiaojian Chen Qiquan Yang Xiong Xv Hui Lin Min Huang Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method International Journal of Applied Earth Observations and Geoinformation One-dimensional convolution (Conv1D) Long short-term memory (LSTM) Time series gridded Argo data Three-dimensional ocean temperature (3D-OT) prediction |
| title | Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method |
| title_full | Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method |
| title_fullStr | Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method |
| title_full_unstemmed | Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method |
| title_short | Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method |
| title_sort | prediction of long lead monthly three dimensional ocean temperature using time series gridded argo data and a deep learning method |
| topic | One-dimensional convolution (Conv1D) Long short-term memory (LSTM) Time series gridded Argo data Three-dimensional ocean temperature (3D-OT) prediction |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222001649 |
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