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.

A Neural Network Model for Forecasting CO2 Emission

GALLO, CRESCENZIO;CONTO', FRANCESCO;FIORE, MARIANTONIETTA
2014-01-01

Abstract

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11369/268968
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? ND
social impact