THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

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Randa Alharbi

,

Abeer D. Algarni

ADVANCED MACHINE LEARNING TECHNIQUES FOR PREDICTING NOX LEVELS

ABSTRACT
This study explores the application of machine learning techniques to forecast atmospheric pollutant concentrations, focusing on NOx, NO2, and NO over the period from January 1, 2017, to December 1, 2017. Accurate prediction of air pollutant levels is crucial for effective environmental monitoring and public health protection. The research employs the Gaussian mixture model and decision tree model to analyze and predict pollutant data. The methodology encompasses rigorous data preprocessing steps, including cleaning and normalization, followed by model training and validation using cross-validation techniques to enhance robustness. Model performance is assessed through multiple metrics, including entropy, log-likelihood, normalized entropy criterion, integrated completed likelihood, akaike information criterion, and Bayesian information criterion. Results demonstrate that the Gaussian mixture model outperforms other approaches in predicting air pollutant levels, offering improved accuracy and reliability for environmental forecasting.
KEYWORDS
air pollutants, machine learning, prediction, decision tree model, Gaussian mixture model, statistical metrics
PAPER SUBMITTED: 2024-06-24
PAPER REVISED: 2024-09-10
PAPER ACCEPTED: 2024-10-12
PUBLISHED ONLINE: 2025-01-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2406979A
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 6, PAGES [4979 - 4989]
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Advanced machine learning techniques for predicting NOx levels

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence