Data-Driven Prediction of Deep-Sea Near-Seabed Currents: A Comparative Analysis of Machine Learning Algorithms

Hairong Bao, Zhixiong Yao, Dongfeng Xu, Jun Wang, Chenghao Yang, Nuan Liu, Yuntian Pang

Deep-sea mining has garnered significant global attention, and accurate prediction of ocean currents plays a critical role in optimizing the design of sediment plume monitoring networks associated with mining activities. Using near-seabed mooring data from the Western Pacific M2 block (Beijing Pioneer polymetallic nodule Exploration Area, BPEA), this study trained four machine learning models—LSTM, XGBoost, ARIMA, and SVR—on current velocity to generate 96 h forecasts. Key findings include the following: LSTM and ARIMA models outperformed XGBoost and SVR in near-seabed current prediction. 1 h ahead forecasts substantially improved accuracy over rolling predictions (an iterative process where predicted values are treated as observed values for subsequent prediction steps), reducing zonal current (east–west component) RMSE from 2.395 cm/s to 1.120 cm/s and meridional current (north–south component) RMSE from 2.024 cm/s to 1.224 cm/s. For practical deployment, 3 h ahead forecasts achieved a zonal current RMSE of 1.412 cm/s.