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Conference Publication

Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Host Publication: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

Authors: T. Do, D. Nguyen, E. Tsiligianni, A. Lopez Aguirre, V. Panzica La Manna, F. Pasveer, W. Philips and N. Deligiannis

Publisher: IEEE

Publication Date: Feb. 2019

Number of Pages: 5


Abstract:

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, eg, meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches.

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Mr. Tien Do

+32 (0)2 629 2930

thdo@etrovub.be

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Mr. Duc Nguyen

+32 (0)02 629 168

mdnguyen@etrovub.be

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Dr. Evangelia Tsiligianni

+32 (0)02 629 168

etsiligi@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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