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

Air Quality, Atmosphere and Health

Volumn 11, Issue 8, 2018, Pages 883-895

  Pak, Unjin ^a,b   Kim, Chungsong ^a,b   Ryu, Unsok ^c   Sok, Kyongjin ^d   Pak, Sungnam ^c  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b KIM CHAEK UNIVERSITY OF TECHNOLOGY   (North Korea)

^c KIM IL SUNG UNIVERSITY   (North Korea)

^d University of Science   (North Korea)

Author keywords

CNN; CNN LSTM; Deep learning; LSTM; Ozone prediction

Indexed keywords

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; CONCENTRATION (COMPOSITION); MEMORY; OZONE; PERFORMANCE ASSESSMENT; PRECISION; PREDICTION; TIME SERIES ANALYSIS;

BEIJING [CHINA]; CHINA;

EID: 85052074367     PISSN: 18739318     EISSN: 18739326     Source Type: Journal    
DOI: 10.1007/s11869-018-0585-1     Document Type: Article

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