Analysis of Influencing Factors of SST in Tropical West Indian Ocean Based on COBE Satellite Data

Tang, Chaoli; Hao, Dewei; Wei, Yuanhuan; Zhao, Fengmei; Han, Lin; Wu, Xin

doi:10.3390/jmse10081057

Cited by 1 publication

(1 citation statement)

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“…This layer is created to decrease the feature map size by decreasing the connection One of the species of deep learning models is the CNN network. This model can learn abstracted features [23] and is practical for analyzing visual images [28,30]. In addition, the CNN model also has a layer that can learn the features of sequence multivariable data, which makes it suitable for any prediction task.…”

Section: Convolutional Neural Network (Cnn)mentioning

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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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: Convolutional Neural Network (Cnn)mentioning

confidence: 99%