Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2018
Abstract
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction and feature analysis of fine-gained air quality.
Keywords
Air pollution, Analytical models, Atmospheric modeling, Deep Learning, Feature Analysis, Feature extraction, Feature Selection, Interpolation, Predictive models, Spatio-temporal Semi-supervised Learning
Discipline
Databases and Information Systems | Environmental Sciences | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
30
Issue
12
First Page
2258
Last Page
2297
ISSN
1041-4347
Identifier
10.1109/TKDE.2018.2823740
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
QI, Zhongang; WANG, Tianchun; SONG, Guojie; HU, Weisong; LI, Xi; and ZHANG, Zhongfei Mark.
Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality. (2018). IEEE Transactions on Knowledge and Data Engineering. 30, (12), 2258-2297.
Available at: https://ink.library.smu.edu.sg/sis_research/3979
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2018.2823740
Included in
Databases and Information Systems Commons, Environmental Sciences Commons, Numerical Analysis and Scientific Computing Commons