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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProgress in Advanced Computing and Intelligent Engineering - Proceedings of ICACIE 2017
EditorsBibudhendu Pati, Chhabi Rani Panigrahi, Arun K. Pujari, Sambit Bakshi, Sudip Misra
PublisherSpringer Verlag
Pages177-188
Number of pages12
ISBN (Print)9789811317071
DOIs
StatePublished - 1 Jan 2019
Event2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017 - Ajmer, India
Duration: 23 Nov 201725 Nov 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume713
ISSN (Print)2194-5357

Conference

Conference2nd International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2017
CountryIndia
CityAjmer
Period23/11/1725/11/17

Keywords

  • Artificial neural network
  • Carbon dioxide
  • Forecasting
  • Multiresolution decomposition
  • Stationary wavelet transform

Fingerprint Dive into the research topics of 'Improved forecasting of CO<sub>2</sub> emissions based on an ANN and multiresolution decomposition'. Together they form a unique fingerprint.

Cite this