Citation

Tan, Yi-Fei and Bhuiyan, Md. Istiaq Ahmed (2025) Predicting Carbon Monoxide (CO) Level in the Air Using Machine Learning Techniques. In: 2025 IEEE Symposium on Industrial Electronics & Applications (ISIEA), 26-27 July 2025, Kuala Lumpur, Malaysia.

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Abstract

Carbon monoxide (CO) is a key component of the atmosphere, significantly contributing to the deterioration of air quality. The increasing concentration of CO in the atmosphere contributes to air pollution, a major global health crisis responsible for millions of premature deaths each year. This study focuses on predicting CO levels using supervised machine learning algorithms. The research utilizes hourly data from the UCI Machine Learning Repository, consisting of over 9,300 instances of CO concentrations recorded between April 2004 to March 2005 of an Italian city. In this research, CO levels are categorised into two levels based on the cutoff value c, and models are trained using various machine learning classifiers, including Decision Tree (DT), Random Forest (RF), Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). For the purpose of evaluating the performance of models, accuracy, precision, recall, and F1score are utilized. The findings reveal that when cutoff value c=2 is used, DT, KNN and RF classifiers achieve an accuracy of 87.23 %. However, when c=6 is used, all classifiers except NB perform with consistently high accuracy, with most models (DT, KNN, LR, RF, SVM and XGBoost) achieving around 98.45 % accuracy. Within all the classifiers when c=6, SVM demonstrates better outcomes compared to other classifiers in terms of precision, recall and F1-score.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Air quality prediction, machine learning, classification, carbon monoxide
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Sep 2025 08:01
Last Modified:	04 Oct 2025 09:28
URII:	http://shdl.mmu.edu.my/id/eprint/14603

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