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

Authors :
Farbod Farhangi
Abolghasem Sadeghi-Niaraki
Jalal Safari Bazargani
Seyed Vahid Razavi-Termeh
Dildar Hussain
Soo-Mi Choi
Source :
Journal of Marine Science and Engineering, Vol 11, Iss 6, p 1136 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
20771312
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Journal of Marine Science and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.33673a8fc6af4b1f882681980d776930
Document Type :
article
Full Text :
https://doi.org/10.3390/jmse11061136