aspg-18-4105-1792480499
20261020071459
American Scientific Publishing Group
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American Scientific Publishing Group
Journal of Intelligent Systems and Internet of Things
JISIoT
2769-786X
2690-6791
2025
17
2
Forecasting CO2 Emissions from Cement Manufacturing with iHOW-Tuned Machine Learning Models
Omnia M.
Osama
Department of Communications & Electronics,Delta Higher Institute of Engineering & Technology , Mansoura, Egypt
El-Sayed M.
El-Rabaie
Faculty of Electronic Engineering , Menoufia University , Department of Electronics and Communications , Menouf 32952, Egypt
Marwa M.
Eid
Faculty of Artificial Intelligence , Delta University for Science and Technology , Mansoura 35111, Egypt; Jadara Research Center , Jadara University , Irbid 21110, Jordan
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Cement production is a major contributor to global CO2 emissions, posing a challenge for climate mitigation efforts. Accurate forecasting of these emissions is vital for guiding policy and industrial decarbonization. This study addresses the need for improved predictive frameworks by developing an optimized ensemble-based machine learning model for CO2 emissions forecasting. The model is trained on a corrected global cement emissions dataset and enhanced through hyperparameter tuning using ten metaheuristic algorithms. Among them, the Improved Henry’s Optimization Algorithm (iHOW) achieved superior performance. The iHOW-optimized model attained an MSE of 1.21×10−6 and R2 of 0.9657, improving over the best baseline model (Gradient Boosting: MSE = 0.0164, R2 = 0.8621) by more than 99%. These results confirm the effectiveness of iHOW in producing accurate and reliable forecasts. The proposed framework offers strong potential for integration into carbon tracking systems and policy support tools.
2025
191
213
4105
https://creativecommons.org/licenses/by/4.0/
10.54216/JISIoT.170212
https://www.americaspg.com/journal/18/article/4105
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https://www.americaspg.com/storage/01758100443.pdf