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

被引：0
|
作者：
Li Y. [1 ,2 ]
Xiao L. [1 ,2 ,3 ]
Wei H. [1 ,2 ,3 ]
Kou Y. [1 ]
机构：
[1] State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai
[2] Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai
[3] SJTU Yazhou Bay Institute of Deepsea Science and Technology, Shanghai Jiao Tong University, Hainan, Sanya
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2023年 / 57卷 / 02期
关键词：
long short-term memory (LSTM) network; online prediction; semi-submersible platform; wave run-up;
D O I：
10.16183/j.cnki.jsjtu.2021.310
中图分类号：
学科分类号：
摘要：
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 arc 6 s and 12 s, the average accuracy of the prediction results arc 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. © 2023 Shanghai Jiao Tong University. All rights reserved.
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页码：161 / 167
页数：6
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