A data-driven approach for predicting depth-averaged velocities in the early stages of underwater glider navigation

Abstract

Predicting the depth-averaged velocities of underwater gliders in the early stages of navigation is crucial for task performance optimization. However, due to the insufficient number of data samples for the depth-averaged velocities of underwater gliders in the early stages of navigation and the complex mechanisms of depth-averaged flow, task performance optimization remains challenging. This study presents a data-driven approach, the CutMix-augmented Extreme Gradient Boosting (CM_XGB) method, to effectively address this issue. Initially, the CutMix method is employed to augment the depth-averaged velocity samples. Then, the XGB method is applied for prediction. The proposed CM_XGB method is compared to five different prediction methods using three real-world depth-averaged velocity datasets and three error evaluation metrics, and its effectiveness in accurately predicting depth-averaged velocities under the same parameter conditions during the early stages of observation missions is demonstrated. This study shows that the CM_XGB method accurately predicts depth-averaged velocities even with limited data. This method is especially useful for underwater glider missions with data challenges in the initial stages. The success of the CM_XGB method highlights its potential for broader applications in oceanographic research and related fields, providing a valuable tool for scientists and researchers working with limited datasets.