research-article

CoupledGT: Coupled Geospatial-temporal Data Modeling for Air Quality Prediction

Authors:

Zhiwen YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 9

Article No.: 135, Pages 1 - 21

https://doi.org/10.1145/3604616

Published: 10 August 2023 Publication History

Abstract

Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.

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Cited By

Jin JZhang JTang JLiang SQu Z(2024)Spatio-Temporal Data Mining with Information Integrity Protection: Graph Signal Based Air Quality PredictionICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448401(5190-5194)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448401
S.K.B SMathivanan SRajadurai HCho JEaswaramoorthy S(2024)A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patternsScientific Reports10.1038/s41598-024-74237-314:1Online publication date: 30-Dec-2024
https://doi.org/10.1038/s41598-024-74237-3
Chen LXu JWu BHuang J(2023)Group-Aware Graph Neural Network for Nationwide City Air Quality ForecastingACM Transactions on Knowledge Discovery from Data10.1145/363171318:3(1-20)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3631713
Show More Cited By

Index Terms

CoupledGT: Coupled Geospatial-temporal Data Modeling for Air Quality Prediction
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 9

November 2023

373 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3604532

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 10 August 2023

Online AM: 19 June 2023

Accepted: 08 June 2023

Revised: 07 March 2023

Received: 12 November 2022

Published in TKDD Volume 17, Issue 9

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National Science Fund for Distinguished Young Scholars
National Key R&D Program of China
National Natural Science Foundation of China
Natural Science Basic Research Program of Shaanxi

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Cited By

Jin JZhang JTang JLiang SQu Z(2024)Spatio-Temporal Data Mining with Information Integrity Protection: Graph Signal Based Air Quality PredictionICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448401(5190-5194)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448401
S.K.B SMathivanan SRajadurai HCho JEaswaramoorthy S(2024)A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patternsScientific Reports10.1038/s41598-024-74237-314:1Online publication date: 30-Dec-2024
https://doi.org/10.1038/s41598-024-74237-3
Chen LXu JWu BHuang J(2023)Group-Aware Graph Neural Network for Nationwide City Air Quality ForecastingACM Transactions on Knowledge Discovery from Data10.1145/363171318:3(1-20)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3631713
Vyas NP. PDas PMahajan Y(2023)The Impact of Air Pollution on Respiratory Health Results: An Analysis of Asthma and COPD in a Population Study2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425187(141-146)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425187

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