Sea Surface Temperature Prediction Approach Based on 3D CNN and LSTM with Attention Mechanism

Qiao, Bo; Wu, Zhongchen; Tang, Zhong; Wu, Gang

doi:10.23919/icact51234.2021.9370514

Cited by 17 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first one is to use spatial data, such as latitude, longitude, and regional features, as input for the model [21,22]. The second approach is to employ convolutional neural networks (CNN) to extract spatial features at different scales, and integrates them with time series prediction models to form a comprehensive spatiotemporal forecasting method [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Graph Memory Neural Network for Sea Surface Temperature Prediction

et al. 2023

View full text Add to dashboard Cite

Sea surface temperature (SST) is a key factor in the marine environment, and its accurate forecasting is important for climatic research, ecological preservation, and economic progression. Existing methods mostly rely on convolutional networks, which encounter difficulties in encoding irregular data. In this paper, allowing for comprehensive encoding of irregular data containing land and islands, we construct a graph structure to represent SST data and propose a graph memory neural network (GMNN). The GMNN includes a graph encoder built upon the iterative graph neural network (GNN) idea to extract spatial relationships within SST data. It not only considers node but also edge information, thereby adequately characterizing spatial correlations. Then, a long short-term memory (LSTM) network is used to capture temporal dynamics in the SST variation process. We choose the data from the Northwest Pacific Ocean to validate GMNN’s effectiveness for SST prediction in different partitions, time scales, and prediction steps. The results show that our model has better performance for both complete and incomplete sea areas compared to other models.

show abstract

Section: Introductionmentioning

confidence: 99%

A Graph Memory Neural Network for Sea Surface Temperature Prediction

et al. 2023

View full text Add to dashboard Cite

show abstract

“…This paper aims to use a deep learning model to predict the SST in the China Seas and their adjacent waters within a 1-to 7day lead time at a spatial resolution of 0.05°to improve the SST prediction accuracy in complex seas. In contrast to the study of Qiao et al (2021), we used the SST interannual mean (SSTM) of three decades as seasonal long-period data. Furthermore, considering the strong interaction between the target SST and the adjacent SST in daily prediction, this study used the SSTA time series as a predictor and constructed a three-dimensional convolutional LSTM (3DConv-LSTM) prediction model based on a MIMO strategy.…”

Section: Introductionmentioning

confidence: 99%

Seven-day sea surface temperature prediction using a 3DConv-LSTM model

Guan

2022

Front. Mar. Sci.

View full text Add to dashboard Cite

Due to the application demand, users have higher expectations for the accuracy and resolution of sea surface temperature (SST) products. Recent advances in deep learning show great advantages in exploiting massive ocean datasets, and provides opportunities for investigating regional SST predictions in an efficiency approach. However, for deep learning-based SST prediction to be adopted by users, the output must be accurate. This paper investigates the 7-day SST prediction over the China seas and their adjacent waters at a 0.05° spatial resolution. To improve the prediction’s accuracy, we designed a deep learning model combining the three-dimensional convolution and long short-term memory under multi-input multi-output strategy. The Operational SST and Sea Ice Analysis (OSTIA) SST anomaly was used as training data. To test the model prediction ability, we verified the predicted results with the Sub-seasonal to Seasonal (S2S) prediction data from 2015 to 2019. Validation of the predicted SSTs using the OSTIA test datasets show that the root-mean-square error increases from 0.27°C to 0.53°C during the 1- to 7-day lead time, with predictability decreases from southeast to northwest in the study area. Furthermore, the comparison of predicted SST and S2S data with Argo shows that our model is slightly more accurate, which can achieve -0.08°C bias, with a standard deviation of 0.35°C for a 1-day lead time and -0.07°C bias, with a standard deviation of 0.59°C for a 7-day lead time. The results indicate that the proposed deep learning model is accurate and can be applied in regional daily SST prediction.

show abstract