ML-based techniques for prediction of Ocean currents for underwater vehicles

Abstract

Dynamic ocean current in real-time plays a significant role for the precise navigation of underwater vehicles. Estimation and prediction of ocean currents with traditional methods such as Navier–Stokes equations, which are computationally very complex and also need huge historical ocean data for developing numerical models. Hence, in this paper Machine Learning, based on less complex and easily deployable regression methods is exercised to identify the best prediction model for ocean currents. Further, all the regression methods performed were compared with the R2 score, Mean Absolute Error (MAE) and Mean Square Error (MSE). Among all methods, the Decision tree regression-based ML method performed best with 84% accuracy with minimal error. Qualitative performance is studied using visualization of data correlation, heat maps are also generated and compared.