Exploring representative samples for modeling of wave buoy motion behavior

• Published in: Ocean engineering 2024-04, Vol.298, p.117259, Article 117259
• Main Authors: Deng, Hongying, Zhu, Jialiang, Li, Xintian, Liu, Yi
• Format: Article
• Language: English
• Subjects: Buoy motion behavior; Gaussian process regression; Ocean environment; Probabilistic information; Sample exploring
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.117259

To collect sufficient data to describe buoy motion behavior in extreme environment conditions is intractable. The conventional data-driven models constructed with randomly selected and limited training samples are unable to perform adequately for exhibiting complex dynamic characteristics. In this work, an active sample exploring strategy is developed to build a reliable prediction model with limited samples. First, an evaluated index is designed to depict both the data distribution characteristics and estimation uncertainty of all test data using a probabilistic model. The representative data are then captured and integrated into the initial training set based on this index, and the updated uncertainty information is used as the criterion to judge the exploring termination. Finally, the initial training set is supplemented to enhance the model performance. Experimental and comparative results demonstrate the superiority of the proposed sample exploring strategy. •An active learning strategy for exploring representative samples is developed for modeling of the buoy motion behavior.•The designed evaluated index describes both the data distribution characteristics and estimation uncertainty.•The informative samples can be actively and sequentially mined and absorbed into the training dataset.•The model performance is substantially enhanced through the efficient direction.•The proposed method can be simply implemented for practical engineering applications.