期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：7
页码：6792-6803
DOI：10.15680/IJIRCCE.2018.0607020
出版社：S&S Publications
摘要：The prediction of wave height is one of the major problems of coastal engineering and coastal structures.
In recent years, advances in the prediction of significant wave height have been considerably developed using flexible
calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored
a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a
significant wave height using machine learning approaches.In this paper, a problem of significant wave height
prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of
significant wave height helps in wave energy converters as well as in ship navigation system. This research will
optimize wave parameters for a fast and efficient wave height prediction. For this Particle Swarm Optimization feature
reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using
neural network, extreme learning machine random forest forecasting and support vector machine forecasting technique.
In this work, performance evaluation metrics such as MSE RMSE and MAE values are decreased and gives better
performance of classification that is compared with existing research’s implemented methodology. From the
experimental results, it is observed that proposed algorithm gives the better prediction results with PSO feature
reduction technique and ELM forecasting techniques and achieved about 0.25 RMSE as well as 0.24 MAE.
