Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events

C. Peláez-Rodríguez, J. Pérez-Aracil, A. Gómez-Orellana, D. Guijo-Rubio, V. Vargas , P. Gutiérrez, C. Hervás-Martínez, S. Salcedo-Sanz

Applied Ocean Research, Vol. 153, pp. 1-18, 2024 Indexed in JCR. Impact factor: 4.3, Position: 3/18 (Q1) in ENGINEERING, OCEAN

Abstract

Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.

Cite this publication
BibTex
@article{pelaez-rodriguez2024fuzzy-based,
    author = {César Peláez-Rodríguez and Jorge Pérez-Aracil and Antonio Manuel Gómez-Orellana and David Guijo-Rubio and Víctor Manuel Vargas and Pedro Antonio Gutiérrez and César Hervás-Martínez and Sancho Salcedo-Sanz},
    title = {Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events},
    journal = {Applied Ocean Research},
    year = {2024},
    volume = {153},
    number = {0},
    pages = {1--18},
    doi = {10.1016/j.apor.2024.104273}
}
APA
Peláez-Rodríguez, C., Pérez-Aracil, J., Gómez-Orellana, A., Guijo-Rubio, D., Vargas, V., Gutiérrez, P., Hervás-Martínez, C., Salcedo-Sanz, S. (2024). Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events. Applied Ocean Research, 153(0), 1-18.
CV
C. Peláez-Rodríguez, J. Pérez-Aracil, A.M. Gómez-Orellana, D. Guijo-Rubio, V.M. Vargas (CA), P.A. Gutiérrez, C. Hervás-Martínez, S. Salcedo-Sanz, (5/8) "Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events", Applied Ocean Research, Vol. 153(0), pp. 1-18, 2024. (Q1, IF: 4.3)
RIS
TY  - JOUR
T1  - Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events
AU  - Peláez-Rodríguez, César
AU  - Pérez-Aracil, Jorge
AU  - Gómez-Orellana, Antonio Manuel
AU  - Guijo-Rubio, David
AU  - Vargas, Víctor Manuel
AU  - Gutiérrez, Pedro Antonio
AU  - Hervás-Martínez, César
AU  - Salcedo-Sanz, Sancho
JO  - Applied Ocean Research
VL  - 153
IS  - 0
SP  - 1
EP  - 18
PY  - 2024
DO  - 10.1016/j.apor.2024.104273
ER  -