Estimating urban ultrafine particle distributions with Gaussian process models

CEUR Workshop Proceedings

Volumn 1142, Issue , 2014, Pages 145-153

  Li, Jason Jingshi ^a   Jutzeler, Arnaud ^b   Faltings, Boi ^b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LAND USE;

GAUSSIAN PROCESS; GAUSSIAN PROCESS MODELS; GENERAL ADDITIVE MODELS; HIGH RESOLUTION; LAND USE REGRESSION; ULTRAFINE PARTICLE; URBAN AIR POLLUTION; URBAN ENVIRONMENTS;

MAPPING;

EID: 84926381241     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. K. Aberer, M. Hauswirth, and A. Salehi. A middleware for fast and exible sensor network deployment. In VLDB, 2006.
2. K. Aberer, S. Sathe, D. Chakraborty, A. Martinoli, G. Barrenetxea, B. Faltings, and L. Thiele. OpenSense: Open community driven sensing of environment. In ACM IWGS, 2010.
3. Edwin V Bonilla, Shengbo Guo, and Scott Sanner. Gaussian process preference elicitation. In NIPS, pages 262-270, 2010.
4. Yanshuai Cao, Marcus A Brubaker, David Fleet, and Aaron Hertzmann. Efficient optimization for sparse gaussian process regression. In NIPS, pages 1097{1105, 2013. URL http://papers.nips.cc/paper/5087-efficient-optimization-for-sparse-gaussian-process-regression.pdf.
5. RJ Carroll, R Chen, EI George, TH Li, HJ Newton, H Schmiediche, and N Wang. Ozone exposure and population density in harris county, texas. Journal of the American Statistical Association, 92(438):392-404, 1997.
6. Wei Chu, Zoubin Ghahramani, Francesco Falciani, and David L Wild. Biomarker discovery in microarray gene expression data with gaussian processes. Bioinformatics, 21(16):3385{3393, 2005.
7. Noel AC Cressie and Noel A Cassie. Statistics for spatial data, volume 900. Wiley New York, 1993.
8. Boi Faltings, Jason Jingshi Li, and Radu Jurca. Incentive mechanisms for community sensing. IEEE Transactions on Computers, 63(1):115-128, 2014.
9. D. Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, and L. Thiele. Pushing the spatio-temporal resolution limit of urban air pollution maps. In Proceedings of the 12th International Conference on Pervasive Computing and Communications (PerCom'14), 2014.
10. Gerard Hoek, Rob Beelen, Kees de Hoogh, Danielle Vienneau, John Gulliver, Paul Fischer, and David Briggs. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Envi- ronment, 42(33):7561- 7578, 2008. ISSN 1352-2310. doi: Http://dx.doi.org/10.1016/j.atmosenv.2008.05.057. URL http://www.sciencedirect.com/science/article/pii/S1352231008005748.
11. Jason Jingshi Li, Boi Faltings, and Radu Jurca. Incentive schemes for community sensing. In The 3rd Interna- Tional Conference in Computational Sustainability, 2012a.
12. Jason Jingshi Li, Boi Faltings, Olga Saukh, David Hasenfratz, and Jan Beutel. Sensing the air we breathe - The opensense zurich dataset. In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI12), Toronto, Canada, July 2012b.
13. Bjarke Mlgaard, Tareq Hussein, Jukka Corander, and Kaarle Hmeri. Forecasting size-fractionated particle number concentrations in the urban atmosphere. Atmospheric Environment, 46:155 -163, 2012. ISSN 1352-2310. doi: Http://dx.doi.org/10.1016/j.atmosenv.2011.10.004. URL http://www.sciencedirect.com/science/article/pii/S1352231011010491.
14. J Monroy, Achim Lilienthal, J Blanco, Javier Gonzalez-Jimenez, and Marco Trincavelli. Calibration of mox gas sensors in open sampling systems based on gaussian processes. In IEEE Sensors'12, pages 1743{1746, 2012.
15. Trung V Nguyen and Edwin V Bonilla. Fast allocation of gaussian process experts. In International Conference on Machine Learning, 2014.
16. Carl Edward Rasmussen and Hannes Nickisch. Gaussian processes for machine learning (gpml) toolbox. J. Mach. Learn. Res., 11:3011-3015, December 2010. ISSN 1532-4435. URL http://dl.acm.org/citation.cfm?id=1756006.1953029.
17. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006. ISBN 0-262-18253-X.
18. Matteo Venanzi, Alex Rogers, and Nicholas R Jennings. Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes. In First AAAI Conference on Human Computation and Crowdsourcing, 2013.
19. World-Health-Organization. Air quality and health. In Fact Sheet No. 313, 2011.