Air Pollutants Prediction Model Based on Gated Recurrent Unit Neural Network
In this paper,aiming at the exitsing methods of ambient air pollutants prediction are based on a single data set and a shallow neural network,and the data information hidden in the time series can't be fully exploited.A time series prediction based on gated recurrent unit network is proposed.Firstly,a filling algorithm is designed for the missing values of time series.Then the su-pervised experiment is set up to adjust the parameters of batch size and training step,training optimization algorithm,network weight initialization and Dropout regularization,and the length and time are comparison of parameters of long short-term memory network.Finally,the verificaton and analysis are carried out,and the parameters are compared with the long-term memory time re-current neural network.The research results show that compared with long-term memory time recurrent neural network,threshold loop unit network not only has a faster training time,but also has a more significant in air pollutant prediction performance,which is a feasible and effective prediction method.
gated recurrent unit networktime recurrent neural networktime seriesdeep learningmissing value algo-rithm
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