PM2.5 CONCENTRATION PREDICTION BASED ON HIERARCHICAL ATTENTION LSTM IN BIG DATA

In order to solve the problem of the relatively low accuracy of current PM2.5 concentration prediction, a PM2.5 concentration prediction based on deep learning in a big data environment is proposed. First, observation data related with PM2.5 concentration are collected and standardized per hour from atmospheric background monitoring station of Zhoukou City, and the features of the data are extracted by principal component analysis (PCA) as the input variables of the model. Then, hierarchical attention long short-term memory network (LSTM) and radial basis function (RBF) neural network are used

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PM2.5 CONCENTRATION PREDICTION BASED ON HIERARCHICAL ATTENTION LSTM IN BIG DATA

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