Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems.

Kang, B. and Xie, S., 2025. Enhanced tidal intrusion in the Barataria Basin, Mississippi River Delta. Journal of Coastal Research, 41(2), 336–346. Charlotte (North Carolina), ISSN 0749-0208. Microtidal deltaic systems, comprising a significant portion of global coastal wetlands, are increasingly vulnerable to the combined effects of accelerated sea-level rise and anthropogenic modifications. This study investigates two decades of tidal propagation trends within Louisiana's Barataria Basin, a representative microtidal delta, using wavelet analysis and cross-spectrum techniques applied to a long-term water-level dataset. Results showed a consistent decrease in the predominant (K1) tidal constituent's attenuation, with amplitude ratios at inland stations increasing by as much as 50% between 2008 and 2022. Furthermore, analysis of tidal phase lag variations indicates an increasing trend in K1 tidal wave celerity, suggesting more efficient inland propagation of diurnal tide. This study highlights the rapidly evolving tidal dynamics in microtidal systems and demonstrates the utility of wavelet-based approaches for quantifying these critical changes.

Tension Leg Platform (TLP)-type Floating Offshore Wind Turbines (FOWTs) rely on continuously pre-tensioned mooring tendons, the integrity of which must be monitored to ensure safe operation. However, direct measurement of tendon tension at submerged locations is difficult in practice. This study investigates a virtual sensing approach in which the effective tension at multiple tendon points—fairlead, middle, and anchor—of a 15 MW TLP-type FOWT is estimated from responses measured at or near the free surface. Three deep learning architectures are comparatively evaluated: a Transformer-encoder-based self-attention network (EN-ATT), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Fully coupled time-domain simulations are used to generate the training and test data, and the models are assessed under both nominal and noisy input conditions across multiple noise levels. The EN-ATT achieves the highest accuracy in terms of RMSE, MAE, and the coefficient of determination under both nominal and noisy conditions. Feature gradient analysis indicates that the self-attention model exhibits greater sensitivity to longer input lags than the recurrent networks. While the EN-ATT does not consistently outperform the recurrent networks for extreme values, the results suggest that self-attention architectures are a promising direction for mooring tension monitoring of TLP-type FOWTs.