Volume 10 • Issue 2 • PP: 15–26 • 2025
Optimizing CO2 Emission Forecasting from Vehicles Using Deep Learning and the Football Optimization Algorithm
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy because of suboptimal hyperparameter configurations. This study enhances CO2 emission forecasting by combining deep learning with metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle-emissions data, with hyperparameters tuned using the Football Optimization Algorithm (FbOA), inspired by cooperative team dynamics in football. Comparative evaluation against eight other optimizers shows that FbOA achieves the best performance. The optimized FbOA–EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming the alternatives reported in the study. These results demonstrate the potential of domain-inspired metaheuristics to improve prediction accuracy. The proposed framework offers a scalable solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.
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