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

Research areas:

• Applications - Renewable Energy

Year:

2024

Type of Publication:

Article

Keywords:

Extreme significant wave height, Energy flux, Ensemble models, Long-term prediction, Explainable artificial intelligence

Authors:

• Peláez-Rodríguez, César
• Pérez-Aracil, Jorge
• Gómez-Orellana, Antonio Manuel
• Guijo-Rubio, David
• Vargas, Víctor Manuel
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César
• Salcedo-Sanz, Sancho

Journal:

Applied Ocean Research

Volume:

153

Pages:

104273

Month:

December

ISSN:

0141-1187

BibTex:

@article{aPOR2024, author = "C{\'e}sar Pel{\'a}ez-Rodr{\'i}guez and Jorge P{\'e}rez-Aracil and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Sancho Salcedo-Sanz", 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.", awards = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", comments = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", doi = "10.1016/j.apor.2024.104273", issn = "0141-1187", journal = "Applied Ocean Research", keywords = "Extreme significant wave height, Energy flux, Ensemble models, Long-term prediction, Explainable artificial intelligence", month = "December", note = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", pages = "104273", title = "{F}uzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events", url = "doi.org/10.1016/j.apor.2024.104273", volume = "153", year = "2024", }

Note:

JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY

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.

Comments:

JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY