MULTI-STEP SEA SURFACE HEIGHT SPATIOTEMPORAL PREDICTION BASED ON PROTOTYPE CORRECTED SPATIOTEMPORAL NETWORK
Sea surface height,as an important ocean observation indicator,has an important impact on marine ecosystem and climate change research.When updating the memory states of the traditional spatiotemporal sequence prediction models based on recurrent neural network(RNN),a key problem would occur:the old memory states will be refreshed immediately,the long-term dependence and change trend of the time series cannot be effectively saved,and the model cannot fully exploit important features in the temporal domain in multi-step marine prediction,resulting in serious accumulation of prediction errors as the prediction time step increases.To solve this problem,a prototype corrected spatiotemporal network(PCST-Net)was designed to achieve accurate end-to-end multi-step spatiotemporal prediction of sea surface height.The PCST-Net adopts an RNN-based network structure and designs a memory state update(MSU)cell as the core cell of the model.The MSU cell utilizes the prototype correction module(PCM)to learn the prototype features of the sea surface height samples,thereby extracting key information in the time domain and correcting the high-dimensional features of the sea surface height at the current time step,alleviating effectively the serious error accumulation problem in multi-step spatiotemporal prediction.In addition,a multi-step information input strategy was proposed to enable the model to obtain more comprehensive and accurate contextual information from a wider range of time steps,thereby improving prediction performance.The proposed model was validated through multi-step spatiotemporal predictions of daily mean sea surface height anomaly(SSHA)data in the tropical Pacific.The results show that the average ERMS(root mean square error),EMA(mean absolute error),and R(Pearson correlation coefficient)of the PCST-Net model for multi-step SSHA spatiotemporal prediction in the next 5 days are 0.634 cm,0.488 cm and 0.995,respectively.This study indicated that the PCST-Net model could accurately predict the spatiotemporal change trend of SSHA,and provided a feasible method for the multi-step sea surface height spatiotemporal prediction model.
marine predictionsea surface heightspatiotemporal predictiondeep learningsatellite remote sensing data
万方数据
维普
