Forecasting a water-surface wave train with artificial intelligence- A case study

Abstract

With the advent of ‘Deep Learning’, Artificial Intelligence (AI) has attracted the attention of researchers in various fields, including ocean engineering. This paper applies AI to forecasting a water-surface wave train. Recurrent neural networks (RNN) are used to forecast both actual wave trains and numerically reproduced irregular wave trains. The specific type of network used here is the Long Short-Term Memory (LSTM) model, which is known to have good properties for time series prediction. The methodology is extended to forecasting the motion responses of a floating body in an irregular wave train. The LSTM is found to generate reasonably accurate forecasts, despite the nonlinearity of the data.