Evaluation of Different AI-Based Wave Phase-Resolved Prediction Methods

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.