Method for predicting depth-averaged current velocities of underwater gliders based on data feature analysis
Source abstract
Abstract
In this paper, the data feature of depth-averaged current velocities (DACVs) derived from underwater gliders is analyzed for the first time. Two features of DACVs have been proposed: one is the complex ingredients and small samples, and the other is the stationarity that occurs as the length of a DACV sequence increases. With these features in mind, a set of methods combining statistical analysis and machine learning are proposed to realize the prediction of DACVs. Four groups of DACV data of different gliders from sea trials in the South China Sea are used to verify the prediction method. Based on three general error criteria, the prediction performance of the proposed model is demonstrated. The persistence method is used as a comparison model. The results show that the prediction methods proposed in this paper are effective.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Underwater Vehicles and Communication Systems · Underwater Acoustics Research · Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Software School, North University of China 1 , Taiyuan 030051, China
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 2 , Shenyang 110169, People’s Republic of China
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 3 , Shenyang 110169, China
School of Mechanical Engineering, North China University of Water Resources and Electric Power 4 , Zhengzhou 450045, China
Funding
National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization · National Natural Science Foundation of China · Natural Science Foundation of Shanxi Province · State Key Laboratory of Robotics
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