国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to fully mine the spatiotemporal feature information between multi-factor data, and solve the problem that the PM2.5 value cannot be accurately predicted under the influence of multiple factors, and proposes a PM2.5 prediction method that combines Seasonal-Trend decomposition procedure based on Loess (STL) algorithm, Convolutional Long Short-Term Memory Network (ConvLSTM) and Gated Recurrent Unit (GRU). First, use STL algorithm to decompose PM2.5 data and fuse the decomposed sequence with other factors; Build ConvLSTM-GRU model, and use Bayesian optimization algorithm to search for super parameters; The fused data is transferred to the ConvLSTM network for time-space feature extraction, and then the extracted feature sequence is transferred to the GRU network for prediction. Compared with the prediction results of ConvLSTM-GRU model, CNN-GRU model and GRU model, the proposed model has the characteristics of small error and good prediction effect.