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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...

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Autores principales: Yang, Jiachen, Huo, Jiaming, He, Jingyi, Xiao, Taiqiu, Chen, Desheng, Li, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
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.
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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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