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2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018)
Barcelona, Spain
Aug. 28, 2018 to Aug. 31, 2018
ISSN: 2473-9928
ISBN: 978-1-5386-6052-2
pp: 393-396
Muhammad Rizal Khaefi , Pulse Lab Jakarta of United Nations Global Pulse
Zakiya Pramestri , Pulse Lab Jakarta of United Nations Global Pulse
Imaduddin Amin , Pulse Lab Jakarta of United Nations Global Pulse
Jong Gun Lee , Pulse Lab Jakarta of United Nations Global Pulse
ABSTRACT
Peatland fire and haze events in Southeast Asia are disasters with trans-boundary implications, having increased in recent years along with rapid deforestation, land clearing and severe dry seasons. Aerosols are emitted in high concentrations from the fires, which degrade air quality and reduce visibility, in turn causing economic, social, health, and environmental problems. During haze events, it is critical for public authorities to have timely information about affected populations. Currently, Indonesian disaster management authorities manage forest and peatland fire and haze events based on satellite data and sensors. They are looking for more real-time information in order to better protect vulnerable populations and environment. This paper explores information on visibility extracted from photos shared on social media to improve forecasting performance for haze severity. Our results show that visibility information can improve forecast accuracy over a baseline approach with common features, namely data from satellites and ground air quality sensors. Furthermore, by using social media photos, our model adds a near real-time property to the forecast model, with potential to improve disaster management and mitigation,
INDEX TERMS
image mining, deep learning, air quality forecast, haze severity inference
CITATION

M. R. Khaefi, Z. Pramestri, I. Amin and J. G. Lee, "Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 393-396.
doi:10.1109/ASONAM.2018.8508698
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