PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization-Reference-Cited by-同舟云学术

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

Published:2013-01 Issue: Volume:443 Page:511-519
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Antanasijević Davor Z.,Pocajt Viktor V.,Povrenović Dragan S.,Ristić Mirjana Đ.,Perić-Grujić Aleksandra A.

Funder

Ministry of Education, Science and Technological Development of the Republic of Serbia

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference42 articles.

1. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations;Abdul-Wahab;Environ Modell Softw,2005

2. Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone;Al-Alawi;Environ Modell Softw,2008

3. Antanasijević D, Pocajt V, Popović I, Redžić N, Ristić M. The Forecasting of Municipal Waste Generation Using Artificial Neural Networks and Sustainability Indicators. Sustain Sci, in press. DOI: http://dx.doi.org/10.1007/s11625-012-0161-9.

4. Measuring the immeasurable — a survey of sustainable indices;Böhringer;Ecol Econ,2007

5. National Italian integrated atmospheric model on air pollution: sensitivity to emission inventory;Ciancarella,2011

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