ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE

Joaquim Augusto Pinto Rodrigues, Luiz Biondi Neto, Pedro Henrique Gouvêa Coelho, João Carlos Correia Baptista Soares de Mello

Abstract

This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO2 (tons of carbon dioxide) resulting from fossil fuels consumption for energetic purposes, as well as those related to changes in the use of land, obtained from deforestation indexes. Economical and population growth index have been considered too. The system modeling took into account the definition of the input parameters for the forecast of the GHG measured in terms of tons of CO2. Three input variables have been used to estimate the total tCO2 one year ahead emissions. The ANFIS Neuro-Fuzzy Intelligent System is a hybrid system that enables learning capability in a Fuzzy inference system to model non-linear and complex processes in a vague information environment. The results indicate the Neural-Fuzzy System produces consistent estimates validated by actual test data.

References

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Paper Citation


in Harvard Style

Augusto Pinto Rodrigues J., Biondi Neto L., Henrique Gouvêa Coelho P. and Correia Baptista Soares de Mello J. (2009). ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 248-250. DOI: 10.5220/0002014402480250


in Bibtex Style

@conference{iceis09,
author={Joaquim Augusto Pinto Rodrigues and Luiz Biondi Neto and Pedro Henrique Gouvêa Coelho and João Carlos Correia Baptista Soares de Mello},
title={ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={248-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002014402480250},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - ESTIMATING GREENHOUSE GAS EMISSIONS USING COMPUTATIONAL INTELLIGENCE
SN - 978-989-8111-85-2
AU - Augusto Pinto Rodrigues J.
AU - Biondi Neto L.
AU - Henrique Gouvêa Coelho P.
AU - Correia Baptista Soares de Mello J.
PY - 2009
SP - 248
EP - 250
DO - 10.5220/0002014402480250