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A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction
Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an sig...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575873/ https://www.ncbi.nlm.nih.gov/pubmed/36262122 http://dx.doi.org/10.7717/peerj-cs.1095 |
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author | Yang, Jiachen Huo, Jiaming He, Jingyi Xiao, Taiqiu Chen, Desheng Li, Yang |
author_facet | Yang, Jiachen Huo, Jiaming He, Jingyi Xiao, Taiqiu Chen, Desheng Li, Yang |
author_sort | Yang, Jiachen |
collection | PubMed |
description | Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1. |
format | Online Article Text |
id | pubmed-9575873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95758732022-10-18 A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction Yang, Jiachen Huo, Jiaming He, Jingyi Xiao, Taiqiu Chen, Desheng Li, Yang PeerJ Comput Sci Artificial Intelligence Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1. PeerJ Inc. 2022-09-30 /pmc/articles/PMC9575873/ /pubmed/36262122 http://dx.doi.org/10.7717/peerj-cs.1095 Text en © 2022 Yang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Yang, Jiachen Huo, Jiaming He, Jingyi Xiao, Taiqiu Chen, Desheng Li, Yang A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title | A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title_full | A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title_fullStr | A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title_full_unstemmed | A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title_short | A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction |
title_sort | dbulstm-adaboost model for sea surface temperature prediction |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575873/ https://www.ncbi.nlm.nih.gov/pubmed/36262122 http://dx.doi.org/10.7717/peerj-cs.1095 |
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