Realtime prediction of dynamic mooring lines responses with LSTM neural network model

Abstract

A Long Short-Term Memory (LSTM) neural network model is developed to provide a real-time calculation tool for monitoring the mooring line responses under the operating condition by using the vessel motion as the only input. A feature extraction method based on first order moment and second order center moment is proposed in the data pre-processing to improve the characteristics of fluctuation information of input data. The effective judging standard of the scope of prediction accuracy regarding the proposed neural network model is defined. The impact of normalization method, neural number, and training sets length on the predicting accuracy are studied. The feasibility of the established LSTM neural network model is verified considering different data relevance between the training sets and validating sets. The results indicate that the mooring line responses can be predicted with high precision based on the vessel motion as input by using the established LSTM neural network model.