Title

Incorporating Spatio-Temporal Smoothness for Air Quality Inference

Abstract

It is well recognized that air quality inference is of great importance for environmental protection. However, due to the limited monitoring stations and various impact factors, e.g., meteorology, traffic volume and human mobility, inference of air quality index (AQI) could be a difficult task. Recently, with the development of new ways for collecting and integrating urban, mobile, and public service data, there is a potential to leverage spatial relatedness and temporal dependencies for better AQI estimation. To that end, in this paper, we exploit a novel spatio-temporal multi-task learning strategy and develop an enhanced framework for AQI inference. Specifically, both time dependence within a single monitoring station, and spatial relatedness across all the stations will be captured, and then well trained with effective optimization to support AQI inference tasks. As air-quality related features from cross-domain data have been extracted and quantified, comprehensive experiments based on real-world datasets validate the effectiveness of our proposed framework with significant margin compared with several state-of-the-art baselines, which support the hypothesis that our spatio-temporal multi-task learning framework could better predict and interpret AQI fluctuation.

Meeting Name

2017 IEEE International Conference on Data Mining, ICDM 2017 (2017: Nov. 18-21, New Orleans, LA)

Department(s)

Computer Science

Comments

This research was partially supported by grants from the National Natural Science Foundation of China (Grant No. U1605251 and No. 61703386), the Anhui Provincial Natural Science Foundation (Grant No. 1708085QF140), and the Fundamental Research Funds for the Central Universities (Grant No. WK2150110006). Also, this research was partially supported by University of Missouri Research Board (UMRB) via the proposal number: 4991.

Keywords and Phrases

Air quality; Learning systems; Air quality indices; Monitoring stations; Multitask learning; Real-world datasets; Spatio temporal; State of the art; Time dependence; Urban computing; Data mining; AQI Prediction; Multi-task Learning

International Standard Book Number (ISBN)

978-1-5386-2449-4

International Standard Serial Number (ISSN)

1550-4786; 2374-8486

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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