Forecasting a water-surface wave train with artificial intelligence (Part 2) – Can the occurrence of freak waves be predicted with AI?

Abstract

In the author's previous paper published under the same title, the author showed that in a water-surface wave train, the peak height of a next-coming wave can be forecasted fairly well with recurrent neural networks (RNN). It has been also shown that the motion peak heights of a floating body in a wave train can be forecasted even better than the forecasting of wave peak heights. As the Part-2 paper, freak events such as extremely large waves or motions that are being reported taking place more often than previously thought are dealt with to investigate if such extreme events could be forecasted in advance with artificial intelligence. Two kinds of wave time series were submitted to the present work. One is that numerically produced in a computer while linearly superposing a certain number of regular waves of different frequencies and amplitudes, in which freak-like waves appeared. The other one is that measured in a real sea collected in a 4-year project conducted by Ship Research Institute (currently National Maritime Research Institute), Japan. By courtesy of the institute, the author could access quite a large amount of digitized raw data of measured wave time series and analyze them in the present study. Quite a few freak-like waves appeared in the data. As the result of the analyses conducted in the present work using the numerically produced wave time series, it has been found that the occurrence timing of freak-like waves as well as their heights could be forecasted well with RNN. On the other hand, the results of the analyses obtained using the wave data actually measured in a real sea suggest the occurrence timing of freak-like waves could be forecasted fairly well with RNN, while the peak heights of freak-like waves predicted with RNN are noticeably lower than their actual heights.