Research and application of a novel hybrid air quality early-warning system: A case study in China

Science of the Total Environment

Volumn 626, Issue , 2018, Pages 1421-1438

  Li, Chen ^a   Zhu, Zhijie ^a  

^a DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Air quality early warning system; Attributes selection; Hybrid model; Interval forecasting; Pollution contaminants prediction

Indexed keywords

AIR QUALITY; CONTAMINATION; DECISION MAKING; EFFICIENCY; HEURISTIC ALGORITHMS; IMPURITIES; LEARNING SYSTEMS; QUALITY CONTROL; ROUGH SET THEORY;

ATTRIBUTES SELECTION; EARLY WARNING SYSTEM; EFFECTIVENESS AND EFFICIENCIES; EXTREME LEARNING MACHINE; HYBRID MODEL; INTERVAL FORECASTING; METEOROLOGICAL DISASTERS; RESEARCH AND APPLICATION;

FORECASTING;

AIR QUALITY; ALGORITHM; ATMOSPHERIC POLLUTION; EARLY WARNING SYSTEM; FORECASTING METHOD; MACHINE LEARNING; POLLUTION MONITORING; PREDICTION;

AIR MONITORING; AIR POLLUTION; AIR QUALITY; ALGORITHM; ARTICLE; CASE STUDY; CHINA; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; DECOMPOSITION; ENVIRONMENTAL PROTECTION; FORECASTING; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SIGNAL PROCESSING; AIR POLLUTANT; ANALYSIS; CITY; DEVICES; ENVIRONMENTAL MONITORING; PARTICULATE MATTER; PROCEDURES; STATISTICS AND NUMERICAL DATA; UNCERTAINTY;

CHINA;

AIR POLLUTANTS; AIR POLLUTION; ALGORITHMS; CHINA; CITIES; ENVIRONMENTAL MONITORING; FORECASTING; PARTICULATE MATTER; UNCERTAINTY;

EID: 85042916445     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2018.01.195     Document Type: Article

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