A time-varying ensemble model for ship motion prediction based on feature selection and clustering methods. (15th February 2023) - Wei, Yunyu; Chen, Zezong; Zhao, Chen; Chen, Xi; He, Jiangheng; Zhang, Chunyang
Abstract: Effective ship motion prediction helps to avoid ship navigation hazards in time. However, there is insufficient attention to potentially valuable information in the multi-factor ship-related data space and the time-varying characteristics of ship motion. In this paper, a time-varying ensemble model based on feature selection and clustering methods is proposed to improve the performance of real-time ship motion prediction. The ensemble model consists of three modules that progressively improve the validity of the model. In Module I, the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm is adopted for feature selection of the original multi-factor data to filter out the feature factors that are useful for ship motion prediction. In Module II, the self-organizing map (SOM) algorithm is applied to cluster the multi-factor data after feature selection to reorganize samples with similar attributes into a limited number of clusters. Then, an ensemble learning model is constructed for each cluster using multiple Elman neural networks and Adaboost techniques. In Module III, a time-varying prediction framework is proposed for real-time prediction of ship motion by combining the ensemble model corresponding to each cluster. An empirical study was conducted using three multifactorial datasets collected from a ship in the South China Sea in December 2020. The results show that the proposed model can be a competitive technique for solving complex multi-factor ship motion predictions and has the potential to be applied to real-time ship motion warning systems. Highlights: Propose a time-varying ensemble model for ship motion prediction. Using a multi-objective optimization algorithm for feature selection. The multi-factor dataset after feature selection is divided into different clusters using the SOM algorithm. Employed the Adaboost-.MRT to dynamically adjust the distribution weights of multiple Elman models to form a ensemble model. Proposed a time-varying prediction framework combining the ensemble learning model.