Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Guo, Xing; He, Jianghai; Wang, Biao; Wu, Jiaji

doi:10.3390/rs14194737

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…Since oceans have extensive and complex dynamic systems, the distribution and variation of SST are dependent on different factors. Multi variable SST prediction is a suitable technique for overcoming this issue [52]. Accordingly, we utilized different parameters for time-series SST prediction.…”

Section: Discussionmentioning

confidence: 99%

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Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Farhangi

Sadeghi‐Niaraki

Bazargani

et al. 2023

JMSE

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Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

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Section: Discussionmentioning

confidence: 99%

“…Results indicated that air pressure and water temperature had similar weights and were significantly more important than wind direction and wind speed. However, there are different statements about important features that affect SST in the literature [52][53][54]. Wind speed affects the vertical heat flux, which could change SST [55].…”

Section: Discussionmentioning

confidence: 99%

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Farhangi

Sadeghi‐Niaraki

Bazargani

et al. 2023

JMSE

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“…Tian [30] proposed an electronic transition-based BBPSO for high-dimensional problems. Guo [31] proposed the TBBPSO algorithm, which enhances the local minimum escape capability of the proposed method. Guo [32] proposed BPSO-CM, where particle swarms are given enhanced global search capabilities.…”

Section: Related Workmentioning

confidence: 99%

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Chen,

et al. 2023

Processes

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Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

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“…Since the ocean is a comprehensive and complex dynamic system, the distribution and variability of SST are affected by a variety of factors. In the contribution by Guo et al, entitled "Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks," a multivariate long short-term memory (LSTM) model is proposed that uses wind speed and air pressure at sea level as inputs along with SST in order to overcome this problem and improve prediction accuracy [12]. In addition, for model optimization, a position encoding matrix and multi-dimensional input are studied.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

Jeon

2023

Remote Sensing

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Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Cited by 9 publications

References 40 publications

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

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