Prediction of maximum daily ozone level using combined neural network and statistical characteristics

Environment International

Volumn 29, Issue 5, 2003, Pages 555-562

  Wang, Wenjian ^a   Lu, Weizhen ^b   Wang, Xiekang ^c   Leung, Andrew Y T ^b  

^a SHANXI UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c SICHUAN UNIVERSITY   (China)

Author keywords

Air quality forecasting; ARBF network; Ozone; Statistical characteristics

Indexed keywords

AIR POLLUTION; AIR QUALITY; ENVIRONMENTAL PROTECTION; METEOROLOGY; NEURAL NETWORKS; PHOTOCHEMICAL REACTIONS; RADIAL BASIS FUNCTION NETWORKS; VEGETATION;

ENVIRONMENTAL RESEARCH;

OZONE;

OZONE;

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; OZONE; PREDICTION; STATISTICAL ANALYSIS;

ACCURACY; AIR POLLUTANT; AIR POLLUTION; AIR QUALITY; AMBIENT AIR; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL RESEARCH; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; ENVIRONMENTAL HEALTH; ENVIRONMENTAL IMPACT; ENVIRONMENTAL MONITORING; FOLIAGE; FORECASTING; HEALTH STATUS; HONG KONG; INFORMATION PROCESSING; METEOROLOGICAL PHENOMENA; MODEL; PHOTOCHEMISTRY; POPULATION DENSITY; PREDICTION; PRIORITY JOURNAL; RELIABILITY; SIMULATION; STATISTICAL ANALYSIS; TIME SERIES ANALYSIS; URBAN AREA;

CHINA; HONG KONG;

EID: 0038201778     PISSN: 01604120     EISSN: 18736750     Source Type: Journal    
DOI: 10.1016/S0160-4120(03)00013-8     Document Type: Article

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