A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction-Reference-Cited by-同舟云学术

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A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction

Published:2018-08-16 Issue:8 Volume:11 Page:883-895
ISSN:1873-9318
Container-title:Air Quality, Atmosphere & Health
language:en
Short-container-title:Air Qual Atmos Health

Author:

Pak Unjin,Kim Chungsong,Ryu Unsok,Sok Kyongjin,Pak Sungnam

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution

Link

http://link.springer.com/article/10.1007/s11869-018-0585-1/fulltext.html

Reference35 articles.

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2. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

3. Bernstein JA, Alexis N, Barnes C, Bernstein IL, Bernstein JA, Nel A, Peden D et al (2004) Health effects of air pollution. J Allergy Clin Immunol 114(5):1116–1123. https://doi.org/10.1016/j.jaci.2004.08.030

4. Biancofiore F, Verdecchia M, Carlo PD, Tomassetti B, Aruffo E, Busilacchio M, Bianco S, Tommaso SD, Colangeli C (2015) Analysis of surface ozone using a recurrent neural network. Sci Total Environ 514:379–387

5. Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360(9341):1233–1242

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