The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model.

Ocean waves are one of the leading environmental factors that cause motion of the ocean’s structure. Wave prediction is of great significance for the safety of marine structures. The deterministic sea wave prediction (DSWP) has been focused on because it provided an accurate temporal wave surface. The propagation velocity of wave components is one of the critical problems in DSWP. In this paper, the research of propagation velocity is focused on. The Taylor expansion to wave number is used to prove that the group velocity is the propagation velocity of wave components. The simulated irregular long crest wave data is generated. Utilizing the simulated data, the calculated wave surfaces based on group velocity are consistent with the simulated results. Meanwhile, the comparisons of calculated results based on the group velocity and phase velocity are given. Then, a tank experiment is set to verify the prediction results. To further investigate the prediction performance under different conditions, the influences of environmental factors, including the wind speed, water depth and sea state are analyzed in this paper.

Ensuring the safe operation of marine structures requires accurate phase-resolved wave prediction. However, current studies mostly focus on single-model verification and lack a systematic comparison of mainstream architectures under multiple environmental factors on a unified experimental benchmark, thus offering limited guidance for engineering practice. To fill this gap, we conducted a systematic wave-tank evaluation that quantifies how sea state levels, directional spectrum, prediction distance and lead time jointly affect model accuracy. Four architectures—RNN, LSTM, GRU, and TCN—were trained on 7 × 7 probe matrices acquired under sea states levels (4–7), two directional spreading coefficients (n = 2 and 6), five prediction distances (6.7–33.3 m), and lead times of 1–30Δt. Root-mean-square error (RMSE) served as the quantitative metric. Among recurrent architectures, RNN-WP achieved the lowest high-frequency error under mild sea states (SS4, RMSE = 0.28 m), LSTM-WP delivered the best overall accuracy (SS4–5, RMSE ≤ 0.37 m), and GRU-WP excelled in the medium to high frequency band (SS4–5, RMSE ≤ 0.31 m), whereas TCN-WP remained most robust at long horizons and severe sea states (SS7, RMSE = 0.42 m). Increasing sea-state severity raised RMSE by 40–90%, while a narrower directional distribution amplified errors under extreme conditions. Prediction distance and lead time altered model ranking, confirming that nonlinearity, directional spreading, distance and temporal horizon are the dominant controlling factors for deep learning phase resolved wave prediction.