Wave Run-Up Prediction of Semi-Submersible Platforms Based on Long Short-Term Memory Network

Abstract

Wave run-up and air-gap are key issues for the safety of semi-submersible platforms. Real-time wave run-up prediction is helpful to ensure the safety of offshore activities. Based on the long short-term memory (LSTM) network, the extreme short term online prediction method is developed for predicting the wave run-up of semi-submersible platforms using wave and motion sequences. With the help of large sets of data from the model test, the LSTM model is trained and tested. The study shows that when the forecast durations are 6 s and 12 s, the average accuracy of the prediction results are 92.90% and 84.09%, and the relative errors of the maximum wave run-up height are lower than or equal to 19.69% and 30.66%, respectively. In addition, the model has a stable and exact prediction of extreme values of wave run-up height when the forecast duration is within 6 s, which confirms its ability to provide valid technical support for the early warning of wave slamming and overtopping during the operation of offshore platforms.

Keywords

• long short-term memory (lstm) network
• wave run-up
• online prediction
• semi-submersible platform