PM2.5 Prediction Based on Spatio-Temporal Deep Learning Model
With the rapid urbanization process,air pollution especially PM2.5 severely affects people's health.Pre-dicting air quality accurately can provide substantial support for air pollution prevention and governments'policy-making.Aiming at the problems in current air quality prediction research,including missing data imputation,spatio-temporal feature extraction,a spatiotemporal deep learning model named C3D-LSTM is proposed based on three-di-mensional convolutional neural network and long and short-time memory network,which extracts the features in the temporal and spatial dimensions using three-dimensional convolution module,learns the long-term temporal depend-ency using long and short time memory network and then predicts the PM2.5 concentration of the target station.The experiments are conducted based on the real dataset from 22 stations of Beijing city and the results show that the pro-posed model is superior over other baseline models on the metrics including mean absolute error,mean square error,and the fitting coefficient.
Air quality predictionConvolutional neural networkRecurrent neural networkDeep learning
NETL
NSTL
万方数据
