EFFECTIVE WAVE HEIGHT PREDICTION BASED ON NON STATIONARY-CNN-TRANSFORMER FOR OCEAN WAVES
Aiming at the problems of wave effective height series volatility and strong randomness,which are difficult to predict accurately,and the model cannot efficiently explore the relationship between deep features,a method of wave effective height prediction based on non-stationary CNN-Transformer model is proposed.Firstly,a stabilization module is used to reduce the non-stationarity of wave time series data.Then,a one-dimensional convolutional neural network is used to extract deep features from the relevant data and create the feature vector.Finally,a Transformer with smoothing attention is used to depict the time-dependent wave height sequences and capture the overall relationship among the sequences.The effective prediction results of the wave height after the inverse normalization process are obtained.The approach eliminates the unstable nature of wave time series data,enhancing data prediction effects.Additionally,it possesses excellent feature extraction capabilities and can effectively handle large-scale time series data.During experiments,seven sets of comparative experiments were conducted with Australian buoys,predicting effective wave heights of 0.5,3,6,12 and 24 hours.The proposed model were thoroughly verified from all angles and in every aspect.The case study findings demonstrate that the precision of the model put forward in this paper is notably enhanced across various time periods.
万方数据
维普
