Marine Drifting Trajectory Prediction Based on LSTM-DNN Algorithm. (2nd July 2022)

• Record Type: Journal Article
• Authors: Li, Xianbin; Wang, Kai; Tang, Min; Qin, Jiangyi; Wu, Peng; Yang, Tingting; Zhang, Haichao
• Other Names: Alamoodi A.H. Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-02
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/7099494
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : In this paper, the long short-term memory with dense neural network (LSTM-DNN) is first introduced to calculate marine drifting trajectory. Based on the Internet of Things technology and the LSTM-DNN algorithm, the marine drifting trajectory model is established. In this model, the information such as wind field, temperature field, ocean current motion field, and target attributes are included, and the influences of the above information on the trajectory model are studied in detail. In order to verify the proposed model, the marine experiments are carried out in the end. The results show that the predicted trajectory data matches well with the experimental trajectory data. By introducing DNN into the algorithm, computational accuracy of drifting trajectory can be significantly improved compared with the conventional LSTM-based prediction model. A detailed comparison of the two algorithms has also been given in the paper. The proposed remote sensing of marine drifting trajectory model can provide a high accurate trajectory prediction and will lead an important guidance in the marine search and rescue work.