We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A New Method of Inland Water Ship Trajectory Prediction Based on Long Short-Term Memory Network Optimized by Genetic Algorithm.
- Authors
Qian, Long; Zheng, Yuanzhou; Li, Lei; Ma, Yong; Zhou, Chunhui; Zhang, Dongfang
- Abstract
Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the ship to effectively avoid collisions. However, prediction accuracy and computing efficiency are crucial issues that need to be worked out at present. To solve these problems, in this paper, the deep long short-term memory network framework (LSTM) and genetic algorithm (GA) are introduced to predict the ship trajectory of inland water. Firstly, the collected actual automatic identification system (AIS) data are preprocessed and a series of typical trajectories are extracted from them; then, the LSTM network is used to predict the typical trajectories in real time. Considering that the hyperparameters of the LSTM network have difficulty obtaining the optimal solution manually, the GA is used to optimize hyperparameters of LSTM; finally, the GA-LSTM trajectory prediction model is constructed with the optimal network parameters and compared with the traditional support vector machine (SVM) model and LSTM model. The experimental results show that the GA-LSTM model effectively improves the accuracy and speed of trajectory prediction, with outstanding performance and good generalization, which possess certain reference values for the development of collision avoidance of unmanned ships.
- Subjects
INLAND water transportation; GENETIC algorithms; NAVAL architecture; SUPPORT vector machines; AUTOMATIC identification; MARITIME pilots; WATER distribution
- Publication
Applied Sciences (2076-3417), 2022, Vol 12, Issue 8, p4073
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app12084073