Prediction of significant wave height based on CNN-STLSTM-CNN model

Significant wave height (SWH) is one of the important parameters of the ocean, and its accurate prediction is of great significance to fishery development, maritime traffic and the marine ecosystem. In order to improve the prediction accuracy of significant wave height, this paper proposes a significant wave height prediction model based on convolutional neural network-spatiotemporal long short-term memory-convolutional neural network (CNN-STLSTM-CNN). The model consists of an Encoder, a Translator and a Decoder. The Encoder extracts the spatial features of the SWH data through the convolutional neural network, the Translator extracts the temporal variation characteristics of the spatial features of the SWH data through the spatiotemporal long short-term memory neural network (STLSTM), and the Decoder reconstructs the prediction results through the transposed convolution module of convolutional neural network. Modeling the two-dimensional significant wave height data in the East China Sea and South China Sea, the experimental results show that RMSE, MAE, M_RMSE and M_MAE values of the CNN-STLSTM-CNN model are lower than the existing methods, which verifies the effectiveness of the CNN-STLSTM-CNN model on SWH prediction.