Improved forecasting of CO2 emissions based on an ANN and multiresolution decomposition

Lida Barba; Nibaldo Rodríguez

doi:10.1007/978-981-13-1708-8_17

Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition

Lida Barba, Nibaldo Rodríguez

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The sustainability of the environment is a shared goal of the United Nations. In this context, the forecast of environmental variables such as carbon dioxide (CO₂) plays an important role for the effective decision making. In this work, it is presented multi-step-ahead forecasting of the CO₂ emissions by means of a hybrid model which combines multiresolution decomposition via stationary wavelet transform (SWT) and an artificial neural network (ANN) to improve the accuracy of a typical neural network. The effectiveness of the proposed hybrid model SWT-ANN is evaluated through the time series of CO₂ per capita emissions of the Andean Community (CAN) countries from 1996 to 2013. The empirical results provide significant evidence about the effectiveness of the proposed hybrid model to explain these phenomena. Projections are presented for supporting the environmental management of countries with similar geographical features and cultural diversity.

Original language	English
Title of host publication	Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017
Editors	Bibudhendu Pati, Chhabi Rani Panigrahi, Arun K. Pujari, Sambit Bakshi, Sudip Misra
Publisher	Springer Verlag
Pages	177-188
Number of pages	12
ISBN (Print)	9789811317071
DOIs	https://doi.org/10.1007/978-981-13-1708-8_17
State	Published - 2019
Event	2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017 - Ajmer, India Duration: 23 Nov 2017 → 25 Nov 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	713
ISSN (Print)	2194-5357

Conference

Conference	2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017
Country/Territory	India
City	Ajmer
Period	23/11/17 → 25/11/17

Keywords

Artificial neural network
Carbon dioxide
Forecasting
Multiresolution decomposition
Stationary wavelet transform

Access to Document

10.1007/978-981-13-1708-8_17

Cite this

Barba, L., & Rodríguez, N. (2019). Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition. In B. Pati, C. R. Panigrahi, A. K. Pujari, S. Bakshi, & S. Misra (Eds.), Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017 (pp. 177-188). (Advances in Intelligent Systems and Computing; Vol. 713). Springer Verlag. https://doi.org/10.1007/978-981-13-1708-8_17

Barba, Lida ; Rodríguez, Nibaldo. / Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition. Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017. editor / Bibudhendu Pati ; Chhabi Rani Panigrahi ; Arun K. Pujari ; Sambit Bakshi ; Sudip Misra. Springer Verlag, 2019. pp. 177-188 (Advances in Intelligent Systems and Computing).

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abstract = "The sustainability of the environment is a shared goal of the United Nations. In this context, the forecast of environmental variables such as carbon dioxide (CO2) plays an important role for the effective decision making. In this work, it is presented multi-step-ahead forecasting of the CO2 emissions by means of a hybrid model which combines multiresolution decomposition via stationary wavelet transform (SWT) and an artificial neural network (ANN) to improve the accuracy of a typical neural network. The effectiveness of the proposed hybrid model SWT-ANN is evaluated through the time series of CO2 per capita emissions of the Andean Community (CAN) countries from 1996 to 2013. The empirical results provide significant evidence about the effectiveness of the proposed hybrid model to explain these phenomena. Projections are presented for supporting the environmental management of countries with similar geographical features and cultural diversity.",

keywords = "Artificial neural network, Carbon dioxide, Forecasting, Multiresolution decomposition, Stationary wavelet transform",

author = "Lida Barba and Nibaldo Rodr{\'i}guez",

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Barba, L & Rodríguez, N 2019, Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition. in B Pati, CR Panigrahi, AK Pujari, S Bakshi & S Misra (eds), Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017. Advances in Intelligent Systems and Computing, vol. 713, Springer Verlag, pp. 177-188, 2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017, Ajmer, India, 23/11/17. https://doi.org/10.1007/978-981-13-1708-8_17

Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition. / Barba, Lida; Rodríguez, Nibaldo.
Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017. ed. / Bibudhendu Pati; Chhabi Rani Panigrahi; Arun K. Pujari; Sambit Bakshi; Sudip Misra. Springer Verlag, 2019. p. 177-188 (Advances in Intelligent Systems and Computing; Vol. 713).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Barba L, Rodríguez N. Improved forecasting of CO₂ emissions based on an ANN and multiresolution decomposition. In Pati B, Panigrahi CR, Pujari AK, Bakshi S, Misra S, editors, Progress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017. Springer Verlag. 2019. p. 177-188. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-13-1708-8_17