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Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.

This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were u...

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Main Authors: Ikram, Rana Muhammad Adnan, Cao, Xinyi, Sadeghifar, Tayeb, Kuriqi, Alban, Kisi, Ozgur, Shahid, Shamsuddin
Format: Article
Language:English
Published: MDPI 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/106828/1/ShamsuddinShahid2023_ImprovingSignificantWaveHeightPrediction.pdf
http://eprints.utm.my/106828/
http://dx.doi.org/10.3390/jmse11061163
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spelling my.utm.1068282024-07-28T06:50:28Z http://eprints.utm.my/106828/ Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm. Ikram, Rana Muhammad Adnan Cao, Xinyi Sadeghifar, Tayeb Kuriqi, Alban Kisi, Ozgur Shahid, Shamsuddin TA Engineering (General). Civil engineering (General) This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period. MDPI 2023-06-01 Article PeerReviewed application/pdf en http://eprints.utm.my/106828/1/ShamsuddinShahid2023_ImprovingSignificantWaveHeightPrediction.pdf Ikram, Rana Muhammad Adnan and Cao, Xinyi and Sadeghifar, Tayeb and Kuriqi, Alban and Kisi, Ozgur and Shahid, Shamsuddin (2023) Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm. Journal of Marine Science and Engineering, 11 (6). pp. 1-20. ISSN 2077-1312 http://dx.doi.org/10.3390/jmse11061163 DOI:10.3390/jmse11061163
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ikram, Rana Muhammad Adnan
Cao, Xinyi
Sadeghifar, Tayeb
Kuriqi, Alban
Kisi, Ozgur
Shahid, Shamsuddin
Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
description This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period.
format Article
author Ikram, Rana Muhammad Adnan
Cao, Xinyi
Sadeghifar, Tayeb
Kuriqi, Alban
Kisi, Ozgur
Shahid, Shamsuddin
author_facet Ikram, Rana Muhammad Adnan
Cao, Xinyi
Sadeghifar, Tayeb
Kuriqi, Alban
Kisi, Ozgur
Shahid, Shamsuddin
author_sort Ikram, Rana Muhammad Adnan
title Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
title_short Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
title_full Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
title_fullStr Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
title_full_unstemmed Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
title_sort improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm.
publisher MDPI
publishDate 2023
url http://eprints.utm.my/106828/1/ShamsuddinShahid2023_ImprovingSignificantWaveHeightPrediction.pdf
http://eprints.utm.my/106828/
http://dx.doi.org/10.3390/jmse11061163
_version_ 1805964973192511488
score 13.211869

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