A novel hybrid approach to mooring tension prediction for semi-submersible offshore platforms

Abstract

The accuracy of mooring tension prediction significantly affects the safety of semi-submersible offshore platform operations and the efficiency of production planning. Nevertheless, traditional single prediction models have difficulty accurately forecasting for complex measured mooring tension data. This work provides a hybrid method for predicting mooring tension on semi-submersible maritime platforms based on VMD, error correction, and the convolutional neural network–long short-term memory (CNN-LSTM) and convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) models. The proposed hybrid prediction model was trained, validated, and tested using monitored mooring tension data acquired from the “Deepsea One” semi-submersible ocean platform in the South China Sea under different orientations and sea conditions. The results showed that the proposed hybrid prediction model for mooring tension on semi-submersible offshore platforms had lower values of the root mean square error, mean absolute error, and mean absolute percentage error and higher values of the coefficient of determination (R2) than almost all of the other comparison models when predicting complex time series data of the actual monitored mooring tension. This indicated that the proposed hybrid prediction model had better precision and stability than other models. Furthermore, this method can be used to anticipate nonlinear, nonstationary time series data in different fields of maritime engineering.