A forecasting model based on ARIMA and artificial neural networks for end–OF–life vehicles

The accelerated growth of the automotive supply network has had an immeasurable impact on the environment, especially relating to reusing and disposal of materials. The appropriate management of End-of-Life Vehicles (ELV) has become an imperative item for achieving sustainable development in the field of interest and it is, therefore, a target of special attention from global economies in recent years. Therefore, the present study aims to estimate the future generation of ELVs to assist decision making and mitigate the global impact of this type of waste on the environment. For this, a hybrid forecasting model was used, based on Autoregressive Integrated Moving Average (ARIMA) methodology and on Artificial Neural Networks (ANN), with a set of temporal data extracted from Brazilian sectoral platforms. The results achieved point to a good convergence of the model, indicating better performance than a naive or trivial prediction. The efficiency obtained by the Nash-Sutcliffe coefficient was 98% and the expectation is that for the year 2030, approximately 5.2 million ELVs will be produced in Brazil, of which only 78 thousand units would be effectively recycled, considering the current vehicle recycling rate in the country. Considering the scarcity of information that supports decision-making in waste management in Brazil, this study may also contribute to the proposition of alternatives that favor the proper management of automotive waste, providing a reference for the formulation and implementation of policies related to ELVs in the country.

• Record URL:
  https://doi.org/10.1016/j.jenvman.2022.115616
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0301479722011896
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03014797
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • de Souza, José Américo Fernandes
  • Silva, Maisa Mendonça
  • Rodrigues, Saulo Guilherme
  • Santos, Simone
  • 0000-0003-1086-9140
• Publication Date: 2022-9-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 115616
• Serial:
  • Journal of Environmental Management
  • Volume: 318
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0301-4797
  • EISSN: 1095-8630
  • Serial URL: http://www.sciencedirect.com/science/journal/03014797

Subject/Index Terms

• TRT Terms: Forecasting; Motor vehicles; Neural networks; Recycling; Sustainable development
• Identifier Terms: Autoregressive Integrated Moving Average (ARIMA)
• Geographic Terms: Brazil
• Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01852294
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2022 11:42AM