Fine-grained air quality monitoring based on gaussian process regression

Yun Cheng, Xiucheng Li, Zhijun Li, Shouxu Jiang, Xiaofan Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


Air quality is attracting more and more attentions in recent years due to the deteriorating environment, and PM2.5 is the main contaminant in a lot of areas. Existing softwares that report the level of PM2.5 can provide only the value in the city level, which may indeed varies greatly among different areas in the city. To help people know about the exact air quality around them, we deployed 51 carefully designed devices to measure the PM2.5 at these places and present a Gaussian Process based inference model to estimate the value at any place. The proposed method is evaluated on the real data and compared to some related methods. The experimental results prove the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
EditorsChu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
PublisherSpringer Verlag
Number of pages9
ISBN (Electronic)9783319126395
StatePublished - 2014
Externally publishedYes
Event21st International Conference on Neural Information Processing, ICONIP 2014 - Kuching, Malaysia
Duration: Nov 3 2014Nov 6 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on Neural Information Processing, ICONIP 2014

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.


  • Gaussian process
  • Non-linear regression
  • PM concentrationmonitoring


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