A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network

Science of the Total Environment

Volumn 496, Issue , 2014, Pages 264-274

  Zhou, Qingping ^a   Jiang, Haiyan ^a   Wang, Jianzhou ^a   Zhou, Jianling ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

Ensemble empirical mode decomposition; General regression neural network; Multiple linear regression; PM2.5; Principal components regression

Indexed keywords

AIR QUALITY; AUTOREGRESSIVE MOVING AVERAGE MODEL; FORECASTING; LINEAR REGRESSION; METEOROLOGY; SIGNAL PROCESSING;

ENSEMBLE EMPIRICAL MODE DECOMPOSITION; GENERAL REGRESSION NEURAL NETWORK; MULTIPLE LINEAR REGRESSIONS; PM2.5; PRINCIPAL COMPONENTS;

NEURAL NETWORKS;

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC MODELING; CONCENTRATION (COMPOSITION); DECOMPOSITION ANALYSIS; EMPIRICAL ANALYSIS; ENSEMBLE FORECASTING; PARTICULATE MATTER; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

AIR QUALITY; ARTICLE; CHINA; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; FORECASTING; GENERAL REGRESSION NEURAL NETWORK; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; MULTIPLE LINEAR REGRESSION ANALYSIS; PARTICULATE MATTER; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL ANALYSIS; AIR POLLUTANT; AIR POLLUTION; ANALYSIS; ARTIFICIAL NEURAL NETWORK; CHEMICAL MODEL; ENVIRONMENTAL MONITORING; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

CHINA; SHAANXI; XIAN;

AIR POLLUTANT; PARTICULATE MATTER;

AIR POLLUTANTS; AIR POLLUTION; CHINA; ENVIRONMENTAL MONITORING; FORECASTING; MODELS, CHEMICAL; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PARTICULATE MATTER;

EID: 84905443514     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2014.07.051     Document Type: Article

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