Modeling and prediction for the Buoy motion characteristics. (1st November 2021)

• Record Type: Journal Article
• Authors: Li, Xintian; Bian, Yujian
• Is Part Of: Ocean engineering. Volume 239(2021)
• Publication Date: 2021-11-01
• Subjects: Ocean environment -- Buoy motion characteristics -- Support vector regression -- Modeling and prediction; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.109880
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: To ensure the stability of buoy systems, it is necessary to predict the buoy motion characteristics in different environment conditions. Unfortunately, an accurate model is still not available for some unknown interaction between waves, currents and winds in oceans. Modeling and designing a suitable buoy for further manufacture often encounters some challenges in practice. In this work, the least squares support vector regression (LSSVR) method is proposed to predict buoy motion characteristics. First, some modeling data are collected from the data processing system of a buoy. Additionally, an evaluation criterion is designed to automatically divide training data into two subsets, corresponding to the normal and extreme conditions. Moreover, two local LSSVR models are constructed using the subsets, respectively. Finally, a suitable model is automatically selected for each new sample. Consequently, with limited modeling samples, different property in the normal and extreme conditions can be effectively captured. Experimental results show the superiority of the proposed method. Highlights: A reliable model is constructed for the modeling and prediction of buoy motion characteristics. An evaluation criterion is designed to divide data into two subsets corresponding to the normal and extreme conditions. The method can improve prediction accuracy and reduce buoy design difficulty by increasing the quantity of input variables. Selecting a suitable model for each new sample can better handle different property of the normal and extreme conditions.