EN
Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance
Abstract
This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. Among these, the Random Forest model demonstrated the highest predictive accuracy, achieving an R² score of 0.8965. Feature importance analysis identified nitrogen oxides (NOX), engine speed (RPM), and hydrocarbons (HC) as the most significant predictors of carbon monoxide emissions. Learning curve analysis provided insights into model generalization and highlighted potential limitations. The study underscores the value of data-driven approaches in optimizing engine design and controlling emissions. The findings contribute to the development of cleaner, more efficient vehicles, supporting sustainability efforts in the automotive industry. This research bridges data science and automotive engineering, offering a framework for advanced emission prediction and control that can be applied to other pollutants and engine types.
Keywords
Ethical Statement
It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.
References
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- Reitz, R. D., et al., “IJER editorial: The future of the internal combustion engine”, International Journal of Engine Research, 21(1), 3-10, 2020.
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Details
Primary Language
English
Subjects
Environmentally Sustainable Engineering , Environmental Engineering (Other)
Journal Section
Research Article
Early Pub Date
March 27, 2025
Publication Date
March 28, 2025
Submission Date
October 10, 2024
Acceptance Date
January 15, 2025
Published in Issue
Year 2025 Volume: 8 Number: 1
APA
Eren, B., & Cesur, İ. (2025). Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance. Sakarya University Journal of Computer and Information Sciences, 8(1), 1-11. https://doi.org/10.35377/saucis...1564937
AMA
1.Eren B, Cesur İ. Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance. SAUCIS. 2025;8(1):1-11. doi:10.35377/saucis.1564937
Chicago
Eren, Beytullah, and İdris Cesur. 2025. “Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance”. Sakarya University Journal of Computer and Information Sciences 8 (1): 1-11. https://doi.org/10.35377/saucis. 1564937.
EndNote
Eren B, Cesur İ (March 1, 2025) Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance. Sakarya University Journal of Computer and Information Sciences 8 1 1–11.
IEEE
[1]B. Eren and İ. Cesur, “Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance”, SAUCIS, vol. 8, no. 1, pp. 1–11, Mar. 2025, doi: 10.35377/saucis...1564937.
ISNAD
Eren, Beytullah - Cesur, İdris. “Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 1, 2025): 1-11. https://doi.org/10.35377/saucis. 1564937.
JAMA
1.Eren B, Cesur İ. Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance. SAUCIS. 2025;8:1–11.
MLA
Eren, Beytullah, and İdris Cesur. “Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, Mar. 2025, pp. 1-11, doi:10.35377/saucis. 1564937.
Vancouver
1.Beytullah Eren, İdris Cesur. Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance. SAUCIS. 2025 Mar. 1;8(1):1-11. doi:10.35377/saucis. 1564937
