Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
By Shengdong Du, Tianrui Li, Yan Yang, Shi-Jinn Horng. IEEE Transactions on Knowledge and Data Engineering, 2018
423 citations recorded by the source corpus.
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.
Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/22d76f68966f54d0ec36fd3381b2c0afea50766c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
Citations in the corpus
- Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting (2021, background): “On the other hand, some work [8, 14, 20] realized the importance of auxiliary features, but they simply linked auxiliary features to the primary feature with the same location and time slot lacking generality and omitting spatial dependencies between multi-faceted features.”
- Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “For early air pollution warning and management, an efficient forecasting approach was studied with a hybrid DL architecture [160], which combined 1-D CNNs and bi-directional LSTM networks to fully extract intrinsic correlation features and interdependence of multivariate time series data acquired fr”