Transfer learning of long short-term memory analysis in significant wave height prediction off the coast of western Tohoku, Japan

A large amount of high quality training datasets is usually required for machine learning to predict wave con-ditions with high precision. However, it is not always easy to obtain such sufficient training datasets, especially on offshore new construction locations. This study evaluated significant wave height (SWH) prediction by applying transfer learning into long short-term memory (LSTM) with one month datasets and pre-trained layers from the nearest ports. SWH was predicted for lead times of 6-, 12-, and 24-h at two locations off the coast of the western Tohoku, Japan. The results showed that the application of transfer learning could improve the accuracy of SWH prediction. For example, the coefficient of determination (R2) of the 6-h lead time SWH prediction off the coasts of Akita and Yamagata improved from 0.685 to 0.807 and from 0.710 to 0.850, respectively. Its accuracy was nearly equivalent to that of previous studies employing LSTM networks trained by almost ten years datasets. Furthermore, the results showed that transferring all layers improved the accuracy compared to transferring a part of the hidden or output layers. We believe the results of this study demonstrate that transfer learning has a superior SWH prediction ability with limited training data.

Automated summary

This study evaluated the prediction of significant wave height using transfer learning in long short-term memory networks. The results showed that transfer learning improved the accuracy of the prediction and transferring all layers was more effective than transferring only part of the layers.