Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2023 , Vol. 57 >Issue 2: 161 - 167

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.310

Naval Architecture, Ocean and Civil Engineering

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

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  • 1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai 200240, China
    3. SJTU Yazhou Bay Institute of Deepsea Science and Technology, Shanghai Jiao Tong University, Sanya 572024, Hainan, China

Received date: 2021-08-18

  Revised date: 2021-09-07

  Accepted date: 2021-09-20

  Online published: 2022-11-25

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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.

Key words： long short-term memory (LSTM) network; wave run-up; online prediction; semi-submersible platform

Cite this article

LI Yan, XIAO Longfei, WEI Handi, KOU Yufeng . Wave Run-Up Prediction of Semi-Submersible Platforms Based on Long Short-Term Memory Network[J]. Journal of Shanghai Jiaotong University, 2023 , 57(2) : 161 -167 . DOI: 10.16183/j.cnki.jsjtu.2021.310

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