基于长短期记忆网络的半潜平台波浪爬升预报
收稿日期: 2021-08-18
修回日期: 2021-09-07
录用日期: 2021-09-20
网络出版日期: 2022-11-25
基金资助
国家自然科学基金项目(52031006);国家自然科学基金项目(51879158);国家重点研发计划课题(2018YFC0309704)
Wave Run-Up Prediction of Semi-Submersible Platforms Based on Long Short-Term Memory Network
Received date: 2021-08-18
Revised date: 2021-09-07
Accepted date: 2021-09-20
Online published: 2022-11-25
波浪爬升问题与半潜式平台安全密切相关,波浪爬升的实时在线预报有助于保障海上作业安全.基于长短期记忆(LSTM)神经网络模型,以波浪和平台运动时间序列为输入,建立半潜式平台波浪爬升高度的极短期在线预报方法.通过平台模型试验获得训练与测试数据,对LSTM模型性能进行检验.结果显示,在提前预报量为6 s和12 s时,波浪爬升高度的平均预报精度分别为92.90%和84.09%,最大值相对误差不高于19.69%和30.66%;同时,模型在提前预报量低于6 s时能够对较大的波浪爬升极值实现准确且稳定的预报,可为海上平台运营过程中波浪砰击和越浪等风险预警提供有效技术支持.
李琰, 肖龙飞, 魏汉迪, 寇雨丰 . 基于长短期记忆网络的半潜平台波浪爬升预报[J]. 上海交通大学学报, 2023 , 57(2) : 161 -167 . DOI: 10.16183/j.cnki.jsjtu.2021.310
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.
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