期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 33, 期 6, 页码 2412-2424出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2954510
关键词
Air quality; Forecasting; Atmospheric modeling; Time series analysis; Deep learning; Predictive models; Data models; Air quality forecasting; deep learning; convolutional neural networks; long short-term memory networks
类别
资金
- National Natural Science Foundation of China [61773324, 61572407]
- Center for Cyber-physical System Innovation from The Featured Areas Research Center Program
- MOST [106-2221-E-011-149-MY2, 108-2218-E-011-006]
This article proposes a novel deep learning model for air quality (mainly PM2.5) forecasting, which improves prediction accuracy by learning spatial-temporal correlation features and interdependence of multivariate air quality related time series data.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this article, 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.
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