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

• carbon monoxide emissions
• machine learning
• random forest
• engine performance optimization
• emission control
• sustainability
• automotive engineering