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

• 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
• Institution: Universiti Teknologi Malaysia

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.