Applying linear and nonlinear models for the estimation of particulate matter variability

Environmental Pollution

Volumn 246, Issue , 2019, Pages 89-98

  Tzanis, Chris G ^a   Alimissis, Anastasios ^a   Philippopoulos, Kostas ^a   Deligiorgi, Despina ^a  

^a UNIVERSITY OF ATHENS   (Greece)

Author keywords

Air pollution; Artificial neural networks; Linear and nonlinear models; Particulate matter; Spatial estimation

Indexed keywords

AIR POLLUTION; AIR QUALITY STANDARDS; ERROR STATISTICS; INVERSE PROBLEMS; LINEAR REGRESSION; NEURAL NETWORKS; NONLINEAR SYSTEMS;

INVERSE DISTANCE WEIGHTING; LEAVE-ONE-OUT CROSS VALIDATIONS; LINEAR AND NONLINEAR MODELS; MODEL-PERFORMANCE STATISTICS; MULTIPLE LINEAR REGRESSIONS; PARTICULATE MATTER; SPATIAL ESTIMATION; WORLD HEALTH ORGANIZATION;

AIR QUALITY;

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; ESTIMATION METHOD; INTERPOLATION; LINEARITY; NONLINEARITY; PARTICULATE MATTER; URBAN ATMOSPHERE;

AIR POLLUTION; AIR QUALITY STANDARD; ARTICLE; ARTIFICIAL NEURAL NETWORK; EUROPEAN UNION; FORECASTING; HUMAN; MONITORING; MULTIPLE LINEAR REGRESSION ANALYSIS; NULL HYPOTHESIS; PARTICULATE MATTER; PROBABILITY; PROPHYLAXIS; STATISTICS; VALIDATION PROCESS; WORLD HEALTH ORGANIZATION; AIR POLLUTANT; ANALYSIS; ENVIRONMENTAL MONITORING; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

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

EID: 85057589215     PISSN: 02697491     EISSN: 18736424     Source Type: Journal    
DOI: 10.1016/j.envpol.2018.11.080     Document Type: Article

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