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

Lida Barba, Nibaldo Rodríguez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProgress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017
EditoresBibudhendu Pati, Chhabi Rani Panigrahi, Arun K. Pujari, Sambit Bakshi, Sudip Misra
EditorialSpringer Verlag
Páginas177-188
Número de páginas12
ISBN (versión impresa)9789811317071
DOI
EstadoPublicada - 2019
Evento2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017 - Ajmer, India
Duración: 23 nov. 201725 nov. 2017

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen713
ISSN (versión impresa)2194-5357

Conferencia

Conferencia2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017
País/TerritorioIndia
CiudadAjmer
Período23/11/1725/11/17

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