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Artificial Intelligence and Machine Learning-Driven Decision-Making
Faculty
Computer Science
Year:
2021
Type of Publication:
ZU Hosted
Pages:
Authors:
Ahmed Salah Mohamed Mostafa
Staff Zu Site
Abstract In Staff Site
Journal:
Computational Intelligence and Neuroscience Hindawi
Volume:
Keywords :
Artificial Intelligence , Machine Learning-Driven Decision-Making
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.
Author Related Publications
Ahmed Salah Mohamed Mostafa, "Usages of Spark Framework with Different Machine Learning Algorithms", Hindawi, 2021
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Ahmed Salah Mohamed Mostafa, "Efficient index-independent approaches for the collective spatial keyword queries", elsevier, 2021
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Ahmed Salah Mohamed Mostafa, "A robust UWSN handover prediction system using ensemble learning", MDPI, 2021
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Ahmed Salah Mohamed Mostafa, "Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods", Tech Science Press, 2021
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Ahmed Salah Mohamed Mostafa, "Lazy-Merge: A Novel Implementation for Indexed Parallel K-Way In-Place Merging", IEEE, 2016
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