Deterministic ship roll forecasting model based on multi-objective data fusion and multi-layer error correction

Abstract

With the increasing frequency of international shipping and marine resources development activities, ship motion prediction plays an increasingly important role in ensuring the safety of offshore operations. However, in the field of ship motion prediction, the deep feature information of raw high-resolution ship motion data, as well as the predictable components in the initial prediction residuals is usually neglected. In this paper, a deterministic ship roll forecasting model based on multi-objective data fusion and multi-layer error correction is proposed. The proposed model consists of three stages, which are data pre-processing stage, multi-objective data fusion forecasting stage, and multi-layer error correction stage. To verify the stability and validity of the proposed model, an experimental study was conducted using three sets of measured ship roll motion data collected in the South China Sea from 2018 to 2020. Taking the 1-step, 5-step, and 10-step predictions of dataset #1 as an example, the RMSE values of the proposed model are 0.0130°, 0.0612°, and 0.0791°, respectively. Through three analytical experiments and four comparison experiments, it is proved that the proposed model is able to obtain accurate deterministic point forecasts, which can better assist the sailor in decision making.