What question did this study set out to answer?

To explore how supervised machine learning techniques can predict air quality based on environmental data.

February 5, 2026

Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques

Key Points

To explore how supervised machine learning techniques can predict air quality based on environmental data.
Utilized historical and current environmental data for model training.
Evaluated multiple algorithms including Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting.
Assessed the impact of pollutants like PM2.5, PM10, NO2, SO2, and CO, along with weather parameters like temperature and humidity.
Machine learning models effectively forecasted air quality with high reliability.
Random Forest algorithm outperformed other models in prediction accuracy.

Abstract

Air pollution is a serious concern for public health, linked to many diseases and an increase in fatalities. To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. This study explores how supervised machine learning methods can help predict air quality based on historicaland current environmental data. We assess the effectiveness of algorithms such as Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting. Important factors like PM2.5, PM10, NO2, SO2, and CO pollution levels are examined, along with weather elements like temperature and humidity. Our findings suggest that machine learning models can reliably forecast air quality, helping manage pollution and protect public health, with Random Forest showing the best results among the models tested.

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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques

Key Points

Abstract

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