A new experimental design to predict carbon dioxide emissions using Boruta feature selection and hybrid support vector regression techniques

Akin P., ÇEMREK F.

International Journal of Global Warming, vol.32, no.3, pp.296-308, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 3
  • Publication Date: 2024
  • Doi Number: 10.1504/ijgw.2024.136513
  • Journal Name: International Journal of Global Warming
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, INSPEC, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.296-308
  • Keywords: Boruta feature selection, CO2 emission, GA, genetic algorithm, hybrid support vectors
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

The problem of CO2 emissions is a critical environmental problem for all countries. Countries want to continue to grow by striking a balance between energy and carbon emissions. This study aims to estimate the CO2 emissions of the G7 countries with a new experimental design. This approach combines hybrid SVR with Boruta feature selection. In order to compare this model, three scenarios are built up. This new experimental approach is the first possible situation. The first scenario is this new experimental design. In the second scenario, only the hybrid SVR is implemented without feature selection. In the third scenario, only SVR was applied. The models are compared with the error terms. The best model is the first scenario with the smallest error values. G7 countries’ CO2 emissions and forecast values are close for 2019 and 2020 with the first scenario. Consequently, the CO2 emission of G7 countries can be predicted by a model for the future period.