Prediction modeling of PM2.5 in subway station based on attention mechanism and CNN-ILSTM
In order to improve the prediction accuracy of PM2.5,a combined prediction model based on convolutional neural network(CNN),improved long short-term memory network(ILSTM)and attention mechanism was proposed.ILSTM removes the output gate in LSTM,improves its input gate and forget gate,and introduces a transition information module(CIM)to prevent oversaturation during learning.One-dimensional convolutional neural network models of feature extraction and the ability to improve both short-term and long-term memory network learning sequence dependent state of different time of the past,the combination of characteristics of PM2.5 concentrations in the future,can effectively simulate the PM2.5 dependent on time and space,and through the attention mechanism automatic weighing the characteristics of the state in the past,to further improve the accuracy of PM2.5 prediction.The experimental results show that the fitting degree of CNN-ILSTM-attention model reaches 98.5%,which is improved by 26%,9.2%and 6.2%,respectively,compared with LSTM model,CNN-LSTM model and CNN-ILSTM model.It has high prediction accuracy and application value.
convolutional neural networkimproved long short-term memory networkPM2.5 concentration predictionattention mechanism
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