Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 2 , PP: 32-51, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Omnia M. Osama 1 * , El-Sayed M. El-Rabaie 2 , Marwa M. Eid 3
Doi: https://doi.org/10.54216/JAIM.100203
Accurate prediction of CO2 emissions from vehicles is essential for environmental regulation and sustainable transport design. Existing models often suffer from limited accuracy due to suboptimal hyperparameter configurations. This s tudy a ims t o e nhance C O2 e mission f orecasting b y c ombining d eep l earning with advanced metaheuristic optimization. An attention-based Encoder LSTM (EALSTM) model is trained on Canadian vehicle emissions data, with hyperparameters tuned using a novel 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 EALSTM model yields an RMSE of 0.00349, MAE of 0.00010, and R2 of 0.984, outperforming all alternatives. These results demonstrate the effectiveness of domain-inspired metaheuristics in improving prediction accuracy. The proposed FbOA-EALSTM framework offers a scalable, accurate solution for emissions modeling and supports data-driven environmental policy and intelligent vehicle technologies.
CO2 Emissions , Metaheuristic Optimizatio , Football Optimization Algorithm (FbOA) , Encoder LSTM (EALSTM) , Transportation Sustainability
[1] Gazi Mahabubul Alam, Sharia Arfin Tanim, Sumit Kanti Sarker, Yutaka Watanobe, Md. Rashedul Islam, M. F. Mridha, and Kamruddin Nur. Deep learning model based prediction of vehicle co2 emissions with explainable ai integration for sustainable environment. Scientific Reports, 15, 01 2025.
[2] Yıldıray Yi˘git and Murat Karabatak. A deep reinforcement learning-based speed optimization system to reduce fuel consumption and emissions for smart cities. Applied Sciences, 15:1545–1545, 02 2025.
[3] Esra’a Alrashydah, Thaar Alqahtani, and Abdulnaser M. Al-Sabaeei. Emissions of conventional and electric vehicles: A comparative sustainability assessment. Sustainability, 17:6839–6839, 07 2025.
[4] Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Edgar N. S´anchez, C.Y. Tang, Mark A. Taylor, Blaine Leonard, and Cathy Wu. Mitigating metropolitan carbon emissions with dynamic eco-driving at scale. Transportation Research Part C Emerging Technologies, 179:105146– 105146, 08 2025.
[5] Ishan Shivansh Bangroo. Ai-based predictive analytic approaches for safeguarding the future of electric/ hybrid vehicles. arXiv (Cornell University), 01 2023.
[6] Qi Wang. Towards zero-emission urban mobility: Leveraging ai and lca for targeted interventions. Building Simulation, 17:1653–1657, 10 2024.
[7] Juraj Kov´aˇc, Peter Malega, Erik Varj´u, Jozef Svetl´ık, and Rudolf Stetuliˇc. Reduction of carbon footprint in mechanical engineering production using a universal simulation model. Applied Sciences, 15:5358– 5358, 05 2025.
[8] Nima Khodadadi, S. K. Towfek, Ahmed Mohamed Zaki, Amal H. Alharbi, Ehsan Khodadadi, Doaa Sami Khafaga, Laith Abualigah, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, and Marwa M. Eid. Predicting normalized difference vegetation index using a deep attention network with bidirectional GRU: a hybrid parametric optimization approach. International Journal of Data Science and Analytics, October 2024.
[9] Matteo B¨ohm, Mirco Nanni, and Luca Pappalardo. Gross polluters and vehicle emissions reduction. Nature Sustainability, 5:699–707, 06 2022.
[10] Samuel Akintomide Ajayi, Charles A. Adams, Gift Dumedah, and Atinuke Adebanji. The impact of vehicle engine characteristics on vehicle exhaust emissions for transport modes in lagos city. Urban Planning and Transport Research, 12, 02 2024.
[11] F. E. Sapnken, K. R. Hong, H. Chopkap Noume, and J. G. Tamba. A grey prediction model optimized by meta-heuristic algorithms and its application in forecasting carbon emissions from road fuel combustion. Energy, 302:131922, 2024.
[12] T. Sujjaviriyasup and K. Pitiruek. A hybridization of modwt-svr-de model emphasizing on noise reduction and optimal parameter selection for prediction of co2 emission in thailand. Cogent Engineering, 11(1):2317540, 2024.
[13] M. Zadmirzaei, F. Hasanzadeh, A. Susaeta, and E. Guti´errez. A novel integrated fuzzy dea–artificial intelligence approach for assessing environmental efficiency and predicting co2 emissions. Soft Computing, 28(1):565–591, 2024. [14] Q. Qiao, H. Eskandari, H. Saadatmand, and M. A. Sahraei. An interpretable multi stage forecasting framework for energy consumption and co2 emissions for the transportation sector. Energy, 286:129499, 2024.
[15] M. Singh and R. K. Dubey. Deep learning model based co2 emissions prediction using vehicle telematics sensors data. IEEE Transactions on Intelligent Vehicles, 8(1):768–777, 2023.
[16] W. Wang and J. Wang. Determinants investigation and peak prediction of co2 emissions in china’s transport sector utilizing bio-inspired extreme learning machine. Environmental Science and Pollution Research International, 28(39):55535–55553, 2021.
[17] M. Emami Javanmard, Y. Tang, Z. Wang, and P. Tontiwachwuthikul. Forecast energy demand, co2 emissions and energy resource impacts for the transportation sector. Applied Energy, 338:120830, 2023.
[18] H. Moayedi, A. Mukhtar, N. B. Khedher, I. Elbadawi, M. B. Amara, Q. TT, and N. Khalilpoor. Forecasting of energy-related carbon dioxide emission using ann combined with hybrid metaheuristic optimization algorithms. Engineering Applications of Computational Fluid Mechanics, 18(1):2322509, 2024.
[19] H. Bakır, U¨ . Ag˘bulut, A. E. Gu¨rel, G. Yıldız, U. Gu¨venc，, M. E. M. Soudagar, A. T. Hoang, B. Deepanraj, G. Saini, and A. Afzal. Forecasting of future greenhouse gas emission trajectory for india using energy and economic indexes with various metaheuristic algorithms. Journal of Cleaner Production, 360:131946, 2022.
[20] M. Almsallti, A. B. Alzubi, and O. R. Adegboye. Hybrid metaheuristic optimized extreme learning machine for sustainability focused co2 emission prediction using globalization-driven indicators. Sustainability, 17(15):6783, 2025.
[21] L. K. Foong, V. Blazek, L. Prokop, S. Misak, F. Atamurotov, and N. Khalilpoor. Improve carbon dioxide emission prediction in the asia and oceania (oecd): nature-inspired optimisation algorithms versus conventional machine learning. Engineering Applications of Computational Fluid Mechanics, 18(1):2391988, 2024.
[22] S. Ma, X. Wang, S. Cheng, Y. Liu, Y. Wang, and J. Wang. Improving carbon dioxide emission predictions through a hybrid model utilising an advanced sparrow search algorithm. Environmental Technology, 46(17):3422–3437, 2025.
[23] A. Bashir, S. I. Haruna, Y. E. Ibrhim, and S. I. Abba. Metaheuristic-enhanced ann framework for compressive strength prediction in concrete with supplementary cementitious materials. Innovative Infrastructure Solutions, 10(9):432, 2025.
[24] M. Salehi Sarbijan and J. Behnamian. Multi-product production routing problem by consideration of outsourcing and carbon emissions: particle swarm optimization. Engineering Optimization, 53(8):1298–1314, 2021.
[25] S. Ding, J. Ye, and Z. Cai. Multi-step carbon emissions forecasting using an interpretable framework of new data preprocessing techniques and improved grey multivariable convolution model. Technological Forecasting and Social Change, 208:123720, 2024.
[26] A. B. Ghorbal, A. Grine, I. Elbatal, E. M. Almetwally,M. M. Eid, and E.-S. M. El-Kenawy. Predicting carbon dioxide emissions using deep learning and ninja metaheuristic optimization algorithm. Scientific Reports, 15(1):4021, 2025.
[27] H. Moayedi, A. Mukhtar, S. Alshammari, M. Boujelbene, I. Elbadawi, Q. T. Thi, and M. Mirzaei. Prediction of co2 emission for the central european countries through five metaheuristic optimization techniques helping multilayer perceptron. Engineering Applications of Computational Fluid Mechanics, 18(1):2327437, 2024.
[28] J. Booma, P. Anitha, S. Amosedinakaran, and A. Bhuvanesh. Real-time electricity capacity expansion planning using chaotic ant lion optimization by minimizing carbon emission. Journal of the Chinese Institute of Engineers, 48(3):239–253, 2025.
[29] Y. Su and C.-C. J. Kuo. On extended long short-term memory and dependent bidirectional recurrent neural network. Neurocomputing, 356:151–161, 2019.
[30] Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735– 1780, 1997.
[31] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.
[32] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681, 1997.
[33] Shun-Ichi Amari. Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Transactions on Computers, C-21(11):1197–1206, 1972.
[34] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386–408, 1958.
[35] E.-S. M. El-Kenawy, F. H. Rizk, A. M. Zaki, M. E. Mohamed, A. Ibrahim, A. A. Abdelhamid, N. Khodadadi, E. M. Almetwally, and M. M. Eid. Football optimization algorithm (fboa): A novel metaheuristic inspired by team strategy dynamics. Journal of Artificial Intelligence and Metaheuristics, pages 21–38, 2024.
[36] R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11:341–359, 1997.
[37] Colin Reeves. Genetic algorithms. In Handbook of Metaheuristics, volume 146, pages 109–139. 2010.
[38] O. Bing¨ol, U. Guvenc, S. Duman, and S. Pac，aci. Stochastic fractal search with chaos. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pages 1–6, 2017.
[39] R. Venkata Rao. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7:19–34, 2016.
[40] K.-H. Han and J.-H. Kim. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 6(6):580–593, 2002. [41] X.-S. Yang. A new metaheuristic bat inspired algorithm. In J. R. Gonz´alez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, editors, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pages 65–74. Springer, 2010.
[42] Dan Simon. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6):702–713, 2008.
[43] S. Mirjalili, S. Mirjalili, and A. Hatamlou. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27, 2015.
