PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization

Science of the Total Environment

Volumn 443, Issue , 2013, Pages 511-519

  Antanasijević, Davor Z ^a   Pocajt, Viktor V ^a   Povrenović, Dragan S ^a   Ristić, Mirjana D ^a   Perić Grujić, Aleksandra A ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Annual PM10 emission forecasting; Multiple linear regression; Neural networks; Principal component regression

Indexed keywords

ARTIFICIAL NEURAL NETWORK MODELS; CRUDE STEEL; DATA SETS; EMISSION DATA; EMISSION ESTIMATION; ENVIRONMENTAL INDICATORS; EU COUNTRIES; GROSS DOMESTIC PRODUCTS; INPUT PARAMETER; INPUT VARIABLES; MEAN ABSOLUTE ERROR; MULTI-LINEAR REGRESSION; MULTIPLE LINEAR REGRESSIONS; NATIONAL LEVEL; NEURAL NETWORKS AND GENETIC ALGORITHMS; PAPER AND PAPERBOARD; PM EMISSIONS; PRINCIPAL COMPONENT REGRESSION; REGIONAL AIR POLLUTION; SAWNWOODS; TRANS-BOUNDARY;

AIR POLLUTION; ALUMINUM; COMPUTER SIMULATION; ENERGY UTILIZATION; FORECASTING; INCINERATION; LINEAR REGRESSION; NEURAL NETWORKS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS;

GENETIC ALGORITHMS;

ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; EMISSION; ENVIRONMENTAL INDICATOR; ERROR ANALYSIS; FORECASTING METHOD; GENETIC ALGORITHM; OPTIMIZATION; PARTICULATE MATTER; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SUSTAINABILITY;

ACCURACY; AIR MONITORING; AIR POLLUTANT; AIR POLLUTION CONTROL; AIR QUALITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FORECASTING; GENETIC ALGORITHM; PARTICULATE MATTER; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

SUIDAE;

EID: 84870298794     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2012.10.110     Document Type: Article

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