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
AuthorsAli, Ahmed, Fathalla, Ahmed, Salah, Ahmad, Bekhit, Mahmoud and Eldesouky, Esraa
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.

Keywordsocean ; observation; machine learning; predictive models
Year01 Jan 2021
JournalComputational Intelligence and Neuroscience  (Delisted by Scopus/WOS as a paper mill)
Journal citation2021, pp. 1-13
PublisherHindawi
ISSN1687-5265
Digital Object Identifier (DOI)https://doi.org/10.1155/2021/8551167
Web address (URL)https://onlinelibrary.wiley.com/doi/10.1155/2021/8551167
Open accessOpen access
Research or scholarlyResearch
Page range1-13
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online27 Nov 2021
Publication process dates
Accepted08 Nov 2021
Deposited14 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 publicationUnited States
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