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Article: Scalable Belief Updating for Urban Air Quality Modeling and Prediction
Title | Scalable Belief Updating for Urban Air Quality Modeling and Prediction |
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Authors | |
Issue Date | 2021 |
Publisher | Association for Computing Machinery. The Journal's web site is located at https://dl.acm.org/journal/tds |
Citation | ACM/IMS Transactions on Data Science, 2021, v. 2 n. 1, p. 1-19 How to Cite? |
Abstract | Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn the statistical models which can capture the dynamics from the historical data and predict air pollution in the future. Furthermore, the large size and heterogeneity of today’s big urban data pose significant challenges on the scalability and flexibility of the statistical models. In this work, we present a scalable belief updating framework that is able to produce reliable predictions, using over millions of historical hourly air pollutant and meteorology records. We also present a non-parametric approach to learn the statistical model which reveals interesting periodical dynamics and correlations of the dataset. Based on the scalable belief update framework and the non-parametric model learning approach, we propose an iterative update algorithm to accelerate Gaussian process, which is notorious for its prohibitive computation with large input data. Finally, we demonstrate how to integrate information from heterogeneous data by regarding the beliefs produced by other models as the informative prior. Numerical examples and experimental results are presented to validate the proposed method. |
Persistent Identifier | http://hdl.handle.net/10722/304674 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Liu, X | - |
dc.contributor.author | Ngai, E | - |
dc.contributor.author | Zachariah, D | - |
dc.date.accessioned | 2021-10-05T02:33:31Z | - |
dc.date.available | 2021-10-05T02:33:31Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | ACM/IMS Transactions on Data Science, 2021, v. 2 n. 1, p. 1-19 | - |
dc.identifier.issn | 2691-1922 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304674 | - |
dc.description.abstract | Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn the statistical models which can capture the dynamics from the historical data and predict air pollution in the future. Furthermore, the large size and heterogeneity of today’s big urban data pose significant challenges on the scalability and flexibility of the statistical models. In this work, we present a scalable belief updating framework that is able to produce reliable predictions, using over millions of historical hourly air pollutant and meteorology records. We also present a non-parametric approach to learn the statistical model which reveals interesting periodical dynamics and correlations of the dataset. Based on the scalable belief update framework and the non-parametric model learning approach, we propose an iterative update algorithm to accelerate Gaussian process, which is notorious for its prohibitive computation with large input data. Finally, we demonstrate how to integrate information from heterogeneous data by regarding the beliefs produced by other models as the informative prior. Numerical examples and experimental results are presented to validate the proposed method. | - |
dc.language | eng | - |
dc.publisher | Association for Computing Machinery. The Journal's web site is located at https://dl.acm.org/journal/tds | - |
dc.relation.ispartof | ACM/IMS Transactions on Data Science | - |
dc.rights | ACM/IMS Transactions on Data Science. Copyright © Association for Computing Machinery. | - |
dc.rights | ©ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn | - |
dc.title | Scalable Belief Updating for Urban Air Quality Modeling and Prediction | - |
dc.type | Article | - |
dc.identifier.email | Ngai, E: chngai@eee.hku.hk | - |
dc.identifier.authority | Ngai, E=rp02656 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1145/3402903 | - |
dc.identifier.hkuros | 325886 | - |
dc.identifier.volume | 2 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 19 | - |
dc.publisher.place | United States | - |