Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

Hassan Salah; Mohamed Elbessa

doi:doi:10.11648/j.eas.20240905.12

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Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

Hassan Salah^*

, Mohamed Elbessa

Published in Engineering and Applied Sciences (Volume 9, Issue 5)

Received: 10 September 2024 Accepted: 27 September 2024 Published: 18 October 2024

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Abstract

Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.

Published in	Engineering and Applied Sciences (Volume 9, Issue 5)
DOI	10.11648/j.eas.20240905.12
Page(s)	106-128
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Sea Wave Parameters, Machine Learning, Support Vector Machine, Kernel Functions, Parametric Models, Significant Wave Height

References

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APA Style

Salah, H., Elbessa, M. (2024). Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Engineering and Applied Sciences, 9(5), 106-128. https://doi.org/10.11648/j.eas.20240905.12

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ACS Style

Salah, H.; Elbessa, M. Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Eng. Appl. Sci. 2024, 9(5), 106-128. doi: 10.11648/j.eas.20240905.12

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AMA Style

Salah H, Elbessa M. Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Eng Appl Sci. 2024;9(5):106-128. doi: 10.11648/j.eas.20240905.12

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@article{10.11648/j.eas.20240905.12,
  author = {Hassan Salah and Mohamed Elbessa},
  title = {Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods
},
  journal = {Engineering and Applied Sciences},
  volume = {9},
  number = {5},
  pages = {106-128},
  doi = {10.11648/j.eas.20240905.12},
  url = {https://doi.org/10.11648/j.eas.20240905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20240905.12},
  abstract = {Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.
},
 year = {2024}
}

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TY  - JOUR
T1  - Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

AU  - Hassan Salah
AU  - Mohamed Elbessa
Y1  - 2024/10/18
PY  - 2024
N1  - https://doi.org/10.11648/j.eas.20240905.12
DO  - 10.11648/j.eas.20240905.12
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 106
EP  - 128
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20240905.12
AB  - Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.

VL  - 9
IS  - 5
ER  -

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Author Information

Hassan Salah

Civil Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt; Special Offshore Structures Department, Engineering for the Petroleum and Process Industries Co., Cairo, Egypt

Contact Email

http://orcid.org/0000-0002-3063-7497
Mohamed Elbessa

Oceanography Department, Faculty of Science, Alexandria University, Alexandria, Egypt

Contact Email

http://orcid.org/0000-0003-1192-5669

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APA Style

Salah, H., Elbessa, M. (2024). Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Engineering and Applied Sciences, 9(5), 106-128. https://doi.org/10.11648/j.eas.20240905.12

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ACS Style

Salah, H.; Elbessa, M. Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Eng. Appl. Sci. 2024, 9(5), 106-128. doi: 10.11648/j.eas.20240905.12

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AMA Style

Salah H, Elbessa M. Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods. Eng Appl Sci. 2024;9(5):106-128. doi: 10.11648/j.eas.20240905.12

Copy | Download

@article{10.11648/j.eas.20240905.12,
  author = {Hassan Salah and Mohamed Elbessa},
  title = {Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods
},
  journal = {Engineering and Applied Sciences},
  volume = {9},
  number = {5},
  pages = {106-128},
  doi = {10.11648/j.eas.20240905.12},
  url = {https://doi.org/10.11648/j.eas.20240905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20240905.12},
  abstract = {Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods

AU  - Hassan Salah
AU  - Mohamed Elbessa
Y1  - 2024/10/18
PY  - 2024
N1  - https://doi.org/10.11648/j.eas.20240905.12
DO  - 10.11648/j.eas.20240905.12
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 106
EP  - 128
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20240905.12
AB  - Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.

VL  - 9
IS  - 5
ER  -

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