Study On Long Term Sea Surface Temperature (SST) Prediction Based On Temporal Convolutional Network (TCN) Method

Feng, Yuan; Sun, Tianying; Li, Chen

doi:10.1145/3472634.3472641

Cited by 5 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, accurate SST prediction helps people cope with climate change and possible natural disasters in advance. Currently, various methods, e.g., physical models, time series methods, and machine learning models [3,4], have been proposed for SST prediction. These methods have different characteristics and perform quite differently at oceanic regions of different SST predictability.…”

Section: Introductionmentioning

confidence: 99%

On Evaluating the Predictability of Sea Surface Temperature Using Entropy

et al. 2023

View full text Add to dashboard Cite

Sea surface temperature (SST) has important impacts on the global ecology, and having a good understanding of the predictability, i.e., the possibility of achieving accurate prediction, of SST can help us monitor the marine environment and climate change, and guide the selection and design of SST prediction methods. However, existing studies for analyzing SST mostly measure the rising or falling trends of SST. To address this issue, we introduce a temporal-correlated entropy to quantify the predictability of SST series from both global coarse-grained and local fine-grained aspects, and make SST prediction with multiple deep learning models to prove the effectiveness of such predictability evaluation method. In addition, we explore the dynamics of SST predictability by dividing the time range of interest into consecutive time periods, evaluating the corresponding predictability of SST for each time period, and analyzing the stability of the predictability of SST over time. According to the experiments, the SST predictability values near the poles and equator are really high. The average SST predictability values of the East China Sea, Bohai Sea, and Antarctic Ocean are 0.719, 0.706, and 0.886, respectively, and the size relationship of the SST predictability in the three local sea areas is consistent with our prediction results using multiple representative SST prediction methods, which corroborates the reliability of the predictability evaluation method. In addition, we found that the SST predictability in the Antarctic Ocean changes more dramatically over time than in the East China Sea and the Bohai Sea. The results of SST predictability and its dynamic analysis indicate that global warming, ocean currents, and human activities all have significant impacts on the predictability of SST.

show abstract

Section: Introductionmentioning

confidence: 99%

On Evaluating the Predictability of Sea Surface Temperature Using Entropy

et al. 2023

View full text Add to dashboard Cite

show abstract

“… Xiao et al (2019) proposed a machine learning method that combined the long and short-term memory deep recursive neural network model with Adaboost algorithm to predict short-term and medium-term daily SST. Feng, Sun & Li (2021) used time-domain convolutional network to achieve short-term small-scale SST prediction of the Indian Ocean. Han et al (2019) used convolutional neural network method to achieve regional prediction of SST, sea surface height and ocean salinity in the Pacific.…”

Section: Introductionmentioning

confidence: 99%

A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction

Yang¹,

Huo²,

He³

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

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.

show abstract