A deep cnn-lstm model for particulate matter (Pm2.5) forecasting in smart cities

Sensors (Switzerland)

Volumn 18, Issue 7, 2018, Pages

  Huang, Chiou Jye ^a   Kuo, Ping Huan ^b  

^a JIANGXI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b NATIONAL PINGTUNG UNIVERSITY   (Taiwan)

Author keywords

Big data analytics; CNN LSTM model; Deep learning; PM2.5 forecasting

Indexed keywords

AIR POLLUTION; BIG DATA; CARDIOVASCULAR SYSTEM; CORRELATION METHODS; DEEP LEARNING; DEEP NEURAL NETWORKS; DISEASES; FORECASTING; MEAN SQUARE ERROR; PARTICLES (PARTICULATE MATTER); SMART CITY; WIND EFFECTS;

BIG DATA ANALYTICS; CARDIO-VASCULAR DISEASE; COAL-FIRED POWER GENERATION; CONVOLUTIONAL NEURAL NETWORKS (CNN); MACHINE LEARNING METHODS; PEARSON CORRELATION COEFFICIENTS; PREVENTION AND CONTROLS; ROOT MEAN SQUARE ERRORS;

LONG SHORT-TERM MEMORY;

EID: 85050087079     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18072220     Document Type: Article

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