Marine data prediction : An evaluation of machine learning, deep learning, and statistical predictive models
Journal article
Ali, Ahmed, Fathalla, Ahmed, Salah, Ahmad, Bekhit, Mahmoud and Eldesouky, Esraa. (2021). Marine data prediction : An evaluation of machine learning, deep learning, and statistical predictive models. Computational Intelligence and Neuroscience (Delisted by Scopus/WOS as a paper mill). 2021, pp. 1-13. https://doi.org/10.1155/2021/8551167
Authors | Ali, Ahmed, Fathalla, Ahmed, Salah, Ahmad, Bekhit, Mahmoud and Eldesouky, Esraa |
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Abstract | Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model. |
Keywords | ocean ; observation; machine learning; predictive models |
Year | 01 Jan 2021 |
Journal | Computational Intelligence and Neuroscience (Delisted by Scopus/WOS as a paper mill) |
Journal citation | 2021, pp. 1-13 |
Publisher | Hindawi |
ISSN | 1687-5265 |
Digital Object Identifier (DOI) | https://doi.org/10.1155/2021/8551167 |
Web address (URL) | https://onlinelibrary.wiley.com/doi/10.1155/2021/8551167 |
Open access | Open access |
Research or scholarly | Research |
Page range | 1-13 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 27 Nov 2021 |
Publication process dates | |
Accepted | 08 Nov 2021 |
Deposited | 14 Jun 2024 |
Additional information | Copyright © 2021 Ahmed Ali et al. |
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
Funding: Prince Sattam bin Abdulaziz University | |
Place of publication | United States |
https://acuresearchbank.acu.edu.au/item/909v2/marine-data-prediction-an-evaluation-of-machine-learning-deep-learning-and-statistical-predictive-models
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Publisher's version
OA_Bekhit_2021_ Marine_data_prediction_an_evaluation _of.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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