Prediction of significant wave height using machine learning and its application to extreme wave analysis

Waves of large size can damage offshore infrastructures and affect marine facilities. In coastal engineering studies, it is essential to have the probability estimates of the most extreme wave height expected during the lifetime of the structure. This study predicts significant wave height using the machine learning (ML) technique with generalized extreme value (GEV) theory and its application to extreme wave analysis. The wind speed, wind direction, sea temperature, and swell height data consisting of wave characteristics for 60 years has been obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). While analyzing extreme waves, the block maxima approach in GEV was used to incorporate the seasonal variations present in the data. The estimated scale parameter, shape parameter, and location parameter of GEV are used in the ML model to predict the significant wave heights along with their return periods. The ML algorithms such as linear regression (LR), artificial neural networks (ANN), and support vector machines (SVM) are evaluated in terms of R2 performance. The model comparison results suggested that the SVM model outperforms the LR and ANN models with an accuracy of 99.80%. Finally, the GEV analysis gives the extreme wave height results of 2.348, 3.470, and 4.713 m with a return period of 5, 20, and 100 yrs, respectively. Hence, the model developed is capable of predicting both significant wave height and extreme waves for the design of coastal structures.