Multi-step Prediction of Ocean Environmental Data Based on Space-Air-Ground-Sea Integration

‍ ‍Multi-step prediction and early warning to assist ocean management and decision making were significant and challenging. Real-time， stable and efficient acquisition of wide-area ocean environment data was the premise to guarantee multi-step prediction performance. The air-space-ground-sea integrated 6G network deployment could effectively improve distributed perception capacity， and provide high-quality data support. As we all know， the temporal dependence between data in multi-step mode was greatly weakened. The paper proposed a multi-step prediction model of ocean environmental data based on multi-stage feature learning. In structure， it mainly included a convolutional neural networks （CNN）， optimally combined LSTMs （OLSTMs）， and a fully connected layer. Here， CNN was used to extract fine-grained features between data， while the multi-LSTM combination method based on particle swarm optimization could effectively capture the temporal dependencies between data （coarse-grained features）. Experimental results show that the prediction performance of our proposal is significantly better than CNN， LSTM， and gated recurrent unit （GRU）， and some statistical verifications are carried out.