Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
Journal article, Peer reviewed
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Original versionLinköping Electronic Conference Proceedings. 2017, 2017 (138), 350-354. 10.3384/ecp17138350
This paper presents a flexible system structure to analyze and model for the potential use of huge ship sensor data to generate efficient ship motion prediction model. The noisy raw data is cleaned using noise reduction, resampling and data continuity techniques. For modeling, a flexible Support Vector Regression (SVR) is proposed to solve regression problem. In the data set, sensitivity analysis is performed to find the strength of input attributes for prediction target. The highly significant attributes are considered for input feature which are mapped into higher dimensional feature using non-linear function, thus SVR model for ship motion prediction is achieved. The prediction results for trajectory and pitch show that the proposed system structure is efficient for the prediction of different ship motion attributes.