Enhanced CO2 Emissions Prediction Using Temporal Fusion Transformer Optimized by Football Optimization Algorithm

Abstract

The accurate prediction of carbon dioxide (CO2) 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 CO2 emissions forecasting. This study systematically investigates the efficacy of ML models for CO2 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.