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Integrating artificial intelligence techniques to enhance the performance of hybrid floating breakwater – wave energy converter analysis

Journal Article

Title: Integrating artificial intelligence techniques to enhance the performance of hybrid floating breakwater – wave energy converter analysis
Author:
Bayan, H.; Elkiki, M.; Abdellah, S.; Rabie, A.; Diab, R.
Publication Date:
Journal:
Journal of Ocean Engineering and Marine Energy
Volume:
12
Publisher:
Springer Nature
Affiliation:
Port Said University, Higher Institute for Engineering and Technology, Mansoura University, Arab East Colleges
Technology:
Wave, Oscillating Water Column
Collection Method:
Lab Data, Modeling
Engineering:
Performance
Language:
English

Document Access

Website:
External Link
Attachment:
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Citation

Bayan, H.; Elkiki, M.; Abdellah, S.; Rabie, A.; Diab, R. (2026). Integrating artificial intelligence techniques to enhance the performance of hybrid floating breakwater – wave energy converter analysis. Journal of Ocean Engineering and Marine Energy, 12https://doi.org/10.1007/s40722-026-00475-4
@article{Bayan-2026-,
author = {Bayan, H and Elkiki, M and Abdellah, S and Rabie, A and Diab, R},
title = {Integrating artificial intelligence techniques to enhance the performance of hybrid floating breakwater – wave energy converter analysis},
journal = {Journal of Ocean Engineering and Marine Energy},
year = {2026},
month = {mar},
publisher = {Springer Nature},
volume = {12},
doi = {10.1007/s40722-026-00475-4},
url = {https://link.springer.com/article/10.1007/s40722-026-00475-4},
keywords = {Wave, Oscillating Water Column, Lab Data, Modeling, Performance},
}
Export Citation to BibTex
TY - JOUR
TI - Integrating artificial intelligence techniques to enhance the performance of hybrid floating breakwater – wave energy converter analysis
AU - Bayan, H
AU - Elkiki, M
AU - Abdellah, S
AU - Rabie, A
AU - Diab, R
T2 - Journal of Ocean Engineering and Marine Energy
AB - This study presents an integrated experimental and computational analysis of hybrid floating breakwaters and Wave Energy Converters (WECs). Laboratory experiments were carried out at the Hydraulic Laboratory at Port Said University using floating breakwaters with different rear-wall designs. Based on the experimental data, a novel Ensemble Floating Breakwaters Prediction (EFBP) model was created by combining three Artificial Intelligence (AI) techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gene Expression Programming (GEP). The ensemble method leverages the complementary strengths of these algorithms by averaging to improve prediction reliability and accuracy. The EFBP model showed exceptional performance (R2 = 0.9928, MSE = 1.4543 × 10⁻4), surpassing all individual models. This research establishes the EFBP as a robust predictive tool for optimizing hybrid floating breakwater-WEC systems, aiding the development of sustainable marine energy technologies.
DA - 2026/03//
PY - 2026
PB - Springer Nature
VL - 12
UR - https://link.springer.com/article/10.1007/s40722-026-00475-4
DO - 10.1007/s40722-026-00475-4
LA - English
KW - Wave
KW - Oscillating Water Column
KW - Lab Data
KW - Modeling
KW - Performance
ER -
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Abstract

This study presents an integrated experimental and computational analysis of hybrid floating breakwaters and Wave Energy Converters (WECs). Laboratory experiments were carried out at the Hydraulic Laboratory at Port Said University using floating breakwaters with different rear-wall designs. Based on the experimental data, a novel Ensemble Floating Breakwaters Prediction (EFBP) model was created by combining three Artificial Intelligence (AI) techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gene Expression Programming (GEP). The ensemble method leverages the complementary strengths of these algorithms by averaging to improve prediction reliability and accuracy. The EFBP model showed exceptional performance (R2 = 0.9928, MSE = 1.4543 × 10⁻4), surpassing all individual models. This research establishes the EFBP as a robust predictive tool for optimizing hybrid floating breakwater-WEC systems, aiding the development of sustainable marine energy technologies.