  Air quality forecasting has been regarded as the key problem of air pollution
early warning and control management. In this paper, we propose a novel deep
learning model for air quality (mainly PM2.5) forecasting, which learns the
spatial-temporal correlation features and interdependence of multivariate air
quality related time series data by hybrid deep learning architecture. Due to
the nonlinear and dynamic characteristics of multivariate air quality time
series data, the base modules of our model include one-dimensional
Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term
Memory networks (Bi-LSTM). The former is to extract the local trend features
and spatial correlation features, and the latter is to learn spatial-temporal
dependencies. Then we design a jointly hybrid deep learning framework based on
one-dimensional CNNs and Bi-LSTM for shared representation features learning of
multivariate air quality related time series data. We conduct extensive
experimental evaluations using two real-world datasets, and the results show
that our model is capable of dealing with PM2.5 air pollution forecasting with
satisfied accuracy.
