Enhanced CO 2 Emissions Prediction Using Temporal Fusion Transformer Optimized by Football Optimization Algorithm

Amel Ali Alhussan and Marwa Metwally ()
Additional contact information
Amel Ali Alhussan: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Marwa Metwally: Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA

Mathematics, 2025, vol. 13, issue 10, 1-47

Abstract: The accurate prediction of carbon dioxide (CO 2 ) emissions from light-duty vehicles is critical for mitigating environmental impacts and enhancing regulatory compliance in the automotive industry. However, challenges such as high-dimensional feature spaces, feature redundancy, and hyperparameter sensitivity limit the efficiency of machine learning (ML) models in CO 2 emissions forecasting. This study systematically investigates the efficacy of ML models for CO 2 emissions prediction using the Fuel Consumption Ratings 2023 dataset, with a particular focus on optimizing feature selection and hyperparameter tuning through metaheuristic techniques. The performance of various ML models, including the Temporal Fusion Transformer (TFT), is evaluated before and after optimization. Initially, the TFT model achieved a root mean squared error (RMSE) of 0.082723421 in the baseline scenario. Feature selection using the Football Optimization Algorithm (FbOA) significantly improved its performance, reducing the RMSE to 0.018798774. Further enhancement through metaheuristic optimization using FbOA resulted in an exceptionally low RMSE of 0.000923, demonstrating substantial gains in predictive accuracy. The findings underscore the impact of metaheuristic-driven feature selection and hyperparameter tuning in optimizing ML models for environmental sustainability applications. This work provides a framework for integrating advanced ML methodologies with optimization techniques, offering policymakers and automotive manufacturers a robust tool for assessing and reducing vehicle emissions.

Keywords: carbon dioxide emissions prediction; machine learning optimization; metaheuristic algorithms; feature selection; fuel consumption modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/10/1627/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/10/1627/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:10:p:1627-:d:1656589

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().