Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
This work addresses air pollution early warning and control management, but it appears incremental as it builds on existing deep learning techniques for a domain-specific application.
The authors tackled air quality forecasting, specifically PM2.5 prediction, by proposing a hybrid deep learning model combining 1D-CNNs and Bi-LSTM to learn spatial-temporal features from multivariate time series data, achieving satisfactory accuracy in experiments on two real-world datasets.
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.