Forecasting mooring tension of offshore platforms based on complete ensemble empirical mode decomposition with adaptive noise and deep learning network

The tension sequence data of offshore platforms mooring cables exhibit randomness, strong nonlinearity, and nonsmoothness. Previous studies have shown that single neural network models, such as long short-term memory (LSTM) and recurrent neural network (RNN) models, can effectively predict nonlinear data. Dealing with nonstationary data presents several challenges, nevertheless, like a noticeable delay in forecast outcomes and significant prediction errors. Aiming to address the aforementioned challenges, this article proposes an innovative hybrid prediction model named CEEMDAN-CLL for predicting mooring tension in semi-submersible platforms based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), frequency-domain analysis, convolutional neural network-long short-term memory (CNN-LSTM), and LSTM. The data used in this study are those monitored during the operation of "DeepSea One", which is the first deepwater semi-submersible production platform in the South China Sea, is taken as the research object and cover a wide range of orientations and sea states.