A Neural Network Model for Forecasting CO2 Emission

Air pollution is today a serious problem, caused mainly by human activity. Classical methods are not considered able to efficiently model complex phenomena as meteorology and air pollution because, usually, they make approximations or too rigid schematisations. Our purpose is a more flexible architecture (artificial neural network model) to implement a short-term CO2 emission forecasting tool applied to the cereal sector in Apulia region – in Southern Italy - to determine how the introduction of cultural methods with less environmental impact acts on a possible pollution reduction.


Issue Date:
Jun 30 2014
Publication Type:
Journal Article
DOI and Other Identifiers:
1804-1930 (Other)
PURL Identifier:
http://purl.umn.edu/182488
Published in:
Volume 06, Number 2
AGRIS on-line Papers in Economics and Informatics
Page range:
31-36
Total Pages:
6
JEL Codes:
GA; IN
Series Statement:
6
2




 Record created 2017-04-01, last modified 2017-12-02

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